Last updated on 2025-04-04 21:54:24 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.3.4 | 4.53 | 218.76 | 223.29 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.3.4 | 3.23 | 150.79 | 154.02 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.3.4 | 367.92 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.3.4 | 365.28 | ERROR | |||
| r-devel-macos-arm64 | 0.3.4 | 220.00 | OK | |||
| r-devel-macos-x86_64 | 0.3.4 | 354.00 | OK | |||
| r-devel-windows-x86_64 | 0.3.4 | 7.00 | 202.00 | 209.00 | OK | |
| r-patched-linux-x86_64 | 0.3.4 | 4.02 | 208.15 | 212.17 | OK | |
| r-release-linux-x86_64 | 0.3.4 | 3.67 | 203.67 | 207.34 | OK | |
| r-release-macos-arm64 | 0.3.4 | 164.00 | OK | |||
| r-release-macos-x86_64 | 0.3.4 | 257.00 | OK | |||
| r-release-windows-x86_64 | 0.3.4 | 7.00 | 207.00 | 214.00 | OK | |
| r-oldrel-macos-arm64 | 0.3.4 | OK | ||||
| r-oldrel-macos-x86_64 | 0.3.4 | 299.00 | OK | |||
| r-oldrel-windows-x86_64 | 0.3.4 | 7.00 | 242.00 | 249.00 | OK |
Version: 0.3.4
Check: tests
Result: ERROR
Running ‘testthat.R’ [152s/199s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rBiasCorrection)
>
> local_edition(3)
>
> test_check("rBiasCorrection")
[20250404_044251.]: Entered 'clean_dt'-Function
[20250404_044251.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044251.]: got experimental data
[20250404_044251.]: Entered 'clean_dt'-Function
[20250404_044251.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044251.]: got calibration data
[20250404_044251.]: ### Starting with regression calculations ###
[20250404_044251.]: Entered 'regression_type1'-Function
[20250404_044252.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044252.]: Logging df_agg: CpG#1
[20250404_044252.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044252.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044252.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044252.]: Entered 'hyperbolic_regression'-Function
[20250404_044252.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044252.]: Entered 'cubic_regression'-Function
[20250404_044252.]: 'cubic_regression': minmax = FALSE
[20250404_044252.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044252.]: Logging df_agg: CpG#2
[20250404_044252.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044252.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044252.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044252.]: Entered 'hyperbolic_regression'-Function
[20250404_044252.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044253.]: Entered 'cubic_regression'-Function
[20250404_044253.]: 'cubic_regression': minmax = FALSE
[20250404_044253.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044253.]: Logging df_agg: CpG#3
[20250404_044253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044253.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044253.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044253.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044253.]: Entered 'cubic_regression'-Function
[20250404_044253.]: 'cubic_regression': minmax = FALSE
[20250404_044253.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044253.]: Logging df_agg: CpG#4
[20250404_044253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044253.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044253.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044253.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044253.]: Entered 'cubic_regression'-Function
[20250404_044253.]: 'cubic_regression': minmax = FALSE
[20250404_044253.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044253.]: Logging df_agg: CpG#5
[20250404_044253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044253.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044253.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044253.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044254.]: Entered 'cubic_regression'-Function
[20250404_044254.]: 'cubic_regression': minmax = FALSE
[20250404_044252.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044252.]: Logging df_agg: CpG#6
[20250404_044252.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044252.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044252.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044252.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044253.]: Entered 'cubic_regression'-Function
[20250404_044253.]: 'cubic_regression': minmax = FALSE
[20250404_044253.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044253.]: Logging df_agg: CpG#7
[20250404_044253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044253.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044253.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044253.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044254.]: Entered 'cubic_regression'-Function
[20250404_044254.]: 'cubic_regression': minmax = FALSE
[20250404_044254.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044254.]: Logging df_agg: CpG#8
[20250404_044254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044254.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044254.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044254.]: Entered 'hyperbolic_regression'-Function
[20250404_044254.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044254.]: Entered 'cubic_regression'-Function
[20250404_044254.]: 'cubic_regression': minmax = FALSE
[20250404_044254.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044254.]: Logging df_agg: CpG#9
[20250404_044254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044254.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044254.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044254.]: Entered 'hyperbolic_regression'-Function
[20250404_044254.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044255.]: Entered 'cubic_regression'-Function
[20250404_044255.]: 'cubic_regression': minmax = FALSE
[20250404_044255.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044255.]: Logging df_agg: row_means
[20250404_044255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044255.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044255.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044255.]: Entered 'hyperbolic_regression'-Function
[20250404_044255.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044255.]: Entered 'cubic_regression'-Function
[20250404_044255.]: 'cubic_regression': minmax = FALSE
[20250404_044259.]: Entered 'regression_type1'-Function
[20250404_044300.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044300.]: Logging df_agg: CpG#1
[20250404_044300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044300.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044300.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044300.]: Entered 'hyperbolic_regression'-Function
[20250404_044300.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044300.]: Entered 'cubic_regression'-Function
[20250404_044300.]: 'cubic_regression': minmax = FALSE
[20250404_044300.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044300.]: Logging df_agg: CpG#2
[20250404_044300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044300.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044300.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044300.]: Entered 'hyperbolic_regression'-Function
[20250404_044300.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044301.]: Entered 'cubic_regression'-Function
[20250404_044301.]: 'cubic_regression': minmax = FALSE
[20250404_044301.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044301.]: Logging df_agg: CpG#3
[20250404_044301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044301.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044301.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044301.]: Entered 'hyperbolic_regression'-Function
[20250404_044301.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044301.]: Entered 'cubic_regression'-Function
[20250404_044301.]: 'cubic_regression': minmax = FALSE
[20250404_044301.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044301.]: Logging df_agg: CpG#4
[20250404_044301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044301.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044301.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044301.]: Entered 'hyperbolic_regression'-Function
[20250404_044301.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044302.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044302.]: Logging df_agg: CpG#5
[20250404_044302.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044302.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044302.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044302.]: Entered 'hyperbolic_regression'-Function
[20250404_044302.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044300.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044300.]: Logging df_agg: CpG#6
[20250404_044300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044300.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044300.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044300.]: Entered 'hyperbolic_regression'-Function
[20250404_044300.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044301.]: Entered 'cubic_regression'-Function
[20250404_044301.]: 'cubic_regression': minmax = FALSE
[20250404_044301.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044301.]: Logging df_agg: CpG#7
[20250404_044301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044301.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044301.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044301.]: Entered 'hyperbolic_regression'-Function
[20250404_044301.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044301.]: Entered 'cubic_regression'-Function
[20250404_044301.]: 'cubic_regression': minmax = FALSE
[20250404_044301.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044301.]: Logging df_agg: CpG#8
[20250404_044301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044301.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044301.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044301.]: Entered 'hyperbolic_regression'-Function
[20250404_044301.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044302.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044302.]: Logging df_agg: CpG#9
[20250404_044302.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044302.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044302.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044302.]: Entered 'hyperbolic_regression'-Function
[20250404_044302.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044302.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044302.]: Logging df_agg: row_means
[20250404_044302.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044302.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044302.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044302.]: Entered 'hyperbolic_regression'-Function
[20250404_044302.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044305.]: Entered 'clean_dt'-Function
[20250404_044305.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044305.]: got experimental data
[20250404_044305.]: Entered 'clean_dt'-Function
[20250404_044305.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044305.]: got calibration data
[20250404_044305.]: ### Starting with regression calculations ###
[20250404_044305.]: Entered 'regression_type1'-Function
[20250404_044305.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044305.]: Logging df_agg: CpG#1
[20250404_044305.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044305.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044305.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044305.]: Entered 'hyperbolic_regression'-Function
[20250404_044305.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044306.]: Entered 'cubic_regression'-Function
[20250404_044306.]: 'cubic_regression': minmax = FALSE
[20250404_044306.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044306.]: Logging df_agg: CpG#2
[20250404_044306.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044306.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044306.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044306.]: Entered 'hyperbolic_regression'-Function
[20250404_044306.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044306.]: Entered 'cubic_regression'-Function
[20250404_044306.]: 'cubic_regression': minmax = FALSE
[20250404_044306.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044306.]: Logging df_agg: CpG#3
[20250404_044306.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044306.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044306.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044306.]: Entered 'hyperbolic_regression'-Function
[20250404_044306.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044307.]: Entered 'cubic_regression'-Function
[20250404_044307.]: 'cubic_regression': minmax = FALSE
[20250404_044307.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044307.]: Logging df_agg: CpG#4
[20250404_044307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044307.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044307.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044307.]: Entered 'hyperbolic_regression'-Function
[20250404_044307.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044307.]: Entered 'cubic_regression'-Function
[20250404_044307.]: 'cubic_regression': minmax = FALSE
[20250404_044307.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044307.]: Logging df_agg: CpG#5
[20250404_044307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044307.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044307.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044307.]: Entered 'hyperbolic_regression'-Function
[20250404_044307.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044308.]: Entered 'cubic_regression'-Function
[20250404_044308.]: 'cubic_regression': minmax = FALSE
[20250404_044306.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044306.]: Logging df_agg: CpG#6
[20250404_044306.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044306.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044306.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044306.]: Entered 'hyperbolic_regression'-Function
[20250404_044306.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044307.]: Entered 'cubic_regression'-Function
[20250404_044307.]: 'cubic_regression': minmax = FALSE
[20250404_044307.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044307.]: Logging df_agg: CpG#7
[20250404_044307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044307.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044307.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044307.]: Entered 'hyperbolic_regression'-Function
[20250404_044307.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044307.]: Entered 'cubic_regression'-Function
[20250404_044307.]: 'cubic_regression': minmax = FALSE
[20250404_044307.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044307.]: Logging df_agg: CpG#8
[20250404_044307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044307.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044307.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044307.]: Entered 'hyperbolic_regression'-Function
[20250404_044307.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044308.]: Entered 'cubic_regression'-Function
[20250404_044308.]: 'cubic_regression': minmax = FALSE
[20250404_044308.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044308.]: Logging df_agg: CpG#9
[20250404_044308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044308.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044308.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044308.]: Entered 'hyperbolic_regression'-Function
[20250404_044308.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044308.]: Entered 'cubic_regression'-Function
[20250404_044308.]: 'cubic_regression': minmax = FALSE
[20250404_044308.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044308.]: Logging df_agg: row_means
[20250404_044308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044308.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044308.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044308.]: Entered 'hyperbolic_regression'-Function
[20250404_044308.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044309.]: Entered 'cubic_regression'-Function
[20250404_044309.]: 'cubic_regression': minmax = FALSE
[20250404_044311.]: Entered 'regression_type1'-Function
[20250404_044312.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044312.]: Logging df_agg: CpG#1
[20250404_044312.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044312.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044312.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044312.]: Entered 'hyperbolic_regression'-Function
[20250404_044312.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044313.]: Entered 'cubic_regression'-Function
[20250404_044313.]: 'cubic_regression': minmax = FALSE
[20250404_044313.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044313.]: Logging df_agg: CpG#2
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044313.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044313.]: Entered 'hyperbolic_regression'-Function
[20250404_044313.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044313.]: Entered 'cubic_regression'-Function
[20250404_044313.]: 'cubic_regression': minmax = FALSE
[20250404_044313.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044313.]: Logging df_agg: CpG#3
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044313.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044313.]: Entered 'hyperbolic_regression'-Function
[20250404_044313.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044314.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044314.]: Logging df_agg: CpG#4
[20250404_044314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044314.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044314.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044314.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044314.]: Logging df_agg: CpG#5
[20250404_044314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044314.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044314.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044312.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044313.]: Logging df_agg: CpG#6
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044313.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044313.]: Entered 'hyperbolic_regression'-Function
[20250404_044313.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044313.]: Entered 'cubic_regression'-Function
[20250404_044313.]: 'cubic_regression': minmax = FALSE
[20250404_044313.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044313.]: Logging df_agg: CpG#7
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044313.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044313.]: Entered 'hyperbolic_regression'-Function
[20250404_044313.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044313.]: Entered 'cubic_regression'-Function
[20250404_044313.]: 'cubic_regression': minmax = FALSE
[20250404_044313.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044313.]: Logging df_agg: CpG#8
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044313.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044314.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044314.]: Logging df_agg: CpG#9
[20250404_044314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044314.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044314.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044314.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044314.]: Logging df_agg: row_means
[20250404_044314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044314.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044314.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044316.]: Entered 'solving_equations'-Function
[20250404_044316.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_044316.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_044316.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_044316.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_044316.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_044316.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_044316.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_044316.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_044316.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 1.13660501904968
[20250404_044316.]: Samplename: 0
Root: 1.137
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.4129696733689
[20250404_044316.]: Samplename: 12.5
Root: 11.413
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.174000526428
[20250404_044316.]: Samplename: 25
Root: 26.174
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.1050449117028
[20250404_044316.]: Samplename: 37.5
Root: 35.105
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.685500330611
[20250404_044316.]: Samplename: 50
Root: 47.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.1440494417104
[20250404_044316.]: Samplename: 62.5
Root: 67.144
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7644668894086
[20250404_044316.]: Samplename: 75
Root: 75.764
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.4054158616395
[20250404_044316.]: Samplename: 87.5
Root: 84.405
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.94827248399
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.948
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_044316.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_044316.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_044316.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_044316.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_044316.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_044316.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_044316.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_044316.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_044316.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_044316.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_044316.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_044316.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_044316.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_044316.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_044316.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_044316.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 2.41558626275183
[20250404_044316.]: Samplename: 0
Root: 2.416
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.1649674907454
[20250404_044316.]: Samplename: 12.5
Root: 10.165
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.9830820412762
[20250404_044316.]: Samplename: 25
Root: 23.983
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2773619900429
[20250404_044316.]: Samplename: 37.5
Root: 37.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.8659386543864
[20250404_044316.]: Samplename: 50
Root: 50.866
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.4342273571069
[20250404_044316.]: Samplename: 62.5
Root: 62.434
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.3915260534323
[20250404_044316.]: Samplename: 75
Root: 76.392
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.159788778566
[20250404_044316.]: Samplename: 87.5
Root: 86.16
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.267759893323
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.268
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_044316.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_044316.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_044316.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_044316.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_044316.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_044316.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_044316.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_044316.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_044316.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_044316.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_044316.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_044316.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_044316.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_044316.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_044316.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_044316.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 2.80068218205093
[20250404_044316.]: Samplename: 0
Root: 2.801
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.27535134596596
[20250404_044316.]: Samplename: 12.5
Root: 9.275
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4762621928197
[20250404_044316.]: Samplename: 25
Root: 25.476
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0122075735416
[20250404_044316.]: Samplename: 37.5
Root: 34.012
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.7842655662325
[20250404_044316.]: Samplename: 50
Root: 51.784
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.6732311906145
[20250404_044316.]: Samplename: 62.5
Root: 64.673
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.4326978859189
[20250404_044316.]: Samplename: 75
Root: 78.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.3427232852719
[20250404_044316.]: Samplename: 87.5
Root: 81.343
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.964406640583
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.964
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: -2.13403721845678
[20250404_044317.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.134
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.5082192457956
[20250404_044317.]: Samplename: 12.5
Root: 10.508
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9164567253388
[20250404_044317.]: Samplename: 25
Root: 26.916
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.8334779159501
[20250404_044317.]: Samplename: 37.5
Root: 36.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.0097895977263
[20250404_044317.]: Samplename: 50
Root: 52.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8930527921581
[20250404_044317.]: Samplename: 62.5
Root: 64.893
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.5671055499357
[20250404_044317.]: Samplename: 75
Root: 74.567
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.5294954832669
[20250404_044317.]: Samplename: 87.5
Root: 84.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.047146466811
[20250404_044317.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.047
--> '100 < root < 110' --> substitute 100
[20250404_044317.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_044317.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_044317.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_044317.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_044317.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_044317.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_044317.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_044317.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_044317.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_044317.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_044317.]: ### Starting with regression calculations ###
[20250404_044317.]: Entered 'regression_type1'-Function
[20250404_044318.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_044318.]: Logging df_agg: CpG#1
[20250404_044318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044318.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_044318.]: Entered 'hyperbolic_regression'-Function
[20250404_044318.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044319.]: Entered 'cubic_regression'-Function
[20250404_044319.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_044319.]: Logging df_agg: CpG#2
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044319.]: Entered 'cubic_regression'-Function
[20250404_044319.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_044319.]: Logging df_agg: CpG#3
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044319.]: Entered 'cubic_regression'-Function
[20250404_044319.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_044319.]: Logging df_agg: CpG#4
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044320.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_044320.]: Logging df_agg: CpG#5
[20250404_044320.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044320.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_044320.]: Entered 'hyperbolic_regression'-Function
[20250404_044320.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_044319.]: Logging df_agg: CpG#6
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044319.]: Entered 'cubic_regression'-Function
[20250404_044319.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_044319.]: Logging df_agg: CpG#7
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044320.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_044320.]: Logging df_agg: CpG#8
[20250404_044320.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044320.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_044320.]: Entered 'hyperbolic_regression'-Function
[20250404_044320.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044320.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_044320.]: Logging df_agg: CpG#9
[20250404_044320.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044320.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_044320.]: Entered 'hyperbolic_regression'-Function
[20250404_044320.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044320.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_044320.]: Logging df_agg: row_means
[20250404_044320.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044320.]: c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_044320.]: Entered 'hyperbolic_regression'-Function
[20250404_044320.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044321.]: Entered 'cubic_regression'-Function
[20250404_044321.]: 'cubic_regression': minmax = FALSE
[20250404_044322.]: Entered 'solving_equations'-Function
[20250404_044322.]: Solving cubic regression for CpG#1
Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 0
Root: 1.334
--> Root in between the borders! Added to results.
Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 12.5
Root: 11.446
--> Root in between the borders! Added to results.
Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 25
Root: 22.228
--> Root in between the borders! Added to results.
Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 37.5
Root: 36.374
--> Root in between the borders! Added to results.
Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 50
Root: 52.044
--> Root in between the borders! Added to results.
Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 62.5
Root: 66.144
--> Root in between the borders! Added to results.
Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 75
Root: 75.864
--> Root in between the borders! Added to results.
Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 87.5
Root: 82.254
--> Root in between the borders! Added to results.
Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.877
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#3
Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 0
Root: 1.441
--> Root in between the borders! Added to results.
Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 12.5
Root: 10.568
--> Root in between the borders! Added to results.
Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 25
Root: 24.796
--> Root in between the borders! Added to results.
Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 37.5
Root: 35.952
--> Root in between the borders! Added to results.
Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 50
Root: 50.684
--> Root in between the borders! Added to results.
Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 62.5
Root: 65.142
--> Root in between the borders! Added to results.
Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 75
Root: 77.905
--> Root in between the borders! Added to results.
Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 87.5
Root: 80.767
--> Root in between the borders! Added to results.
Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.38
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#4
Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 0
Root: 0.858
--> Root in between the borders! Added to results.
Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 12.5
Root: 12.086
--> Root in between the borders! Added to results.
Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 25
Root: 23.316
--> Root in between the borders! Added to results.
Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 37.5
Root: 37.383
--> Root in between the borders! Added to results.
Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 50
Root: 48.353
--> Root in between the borders! Added to results.
Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 62.5
Root: 68.082
--> Root in between the borders! Added to results.
Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 75
Root: 74.615
--> Root in between the borders! Added to results.
Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 87.5
Root: 83.254
--> Root in between the borders! Added to results.
Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.715
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#6
Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 0
Root: 0.349
--> Root in between the borders! Added to results.
Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 12.5
Root: 11.718
--> Root in between the borders! Added to results.
Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 25
Root: 26.396
--> Root in between the borders! Added to results.
Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 37.5
Root: 35.301
--> Root in between the borders! Added to results.
Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 50
Root: 50.134
--> Root in between the borders! Added to results.
Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 62.5
Root: 64.993
--> Root in between the borders! Added to results.
Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 75
Root: 73.639
--> Root in between the borders! Added to results.
Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 87.5
Root: 87.043
--> Root in between the borders! Added to results.
Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.329
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#7
Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 0
Root: 2.13
--> Root in between the borders! Added to results.
Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 12.5
Root: 9.973
--> Root in between the borders! Added to results.
Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 25
Root: 22.206
--> Root in between the borders! Added to results.
Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 37.5
Root: 35.814
--> Root in between the borders! Added to results.
Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 50
Root: 53.28
--> Root in between the borders! Added to results.
Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 62.5
Root: 66.598
--> Root in between the borders! Added to results.
Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 75
Root: 76.575
--> Root in between the borders! Added to results.
Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 87.5
Root: 80.219
--> Root in between the borders! Added to results.
Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.506
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for row_means
Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 0
Root: 1.287
--> Root in between the borders! Added to results.
Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 12.5
Root: 10.847
--> Root in between the borders! Added to results.
Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 25
Root: 24.737
--> Root in between the borders! Added to results.
Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 37.5
Root: 36.02
--> Root in between the borders! Added to results.
Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 50
Root: 50.639
--> Root in between the borders! Added to results.
Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 62.5
Root: 65.497
--> Root in between the borders! Added to results.
Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 75
Root: 75.751
--> Root in between the borders! Added to results.
Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 87.5
Root: 83.403
--> Root in between the borders! Added to results.
Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.573
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: ### Starting with regression calculations ###
[20250404_044322.]: Entered 'regression_type1'-Function
[20250404_044323.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_044323.]: Logging df_agg: CpG#1
[20250404_044323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044323.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_044323.]: Entered 'hyperbolic_regression'-Function
[20250404_044323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044324.]: Entered 'cubic_regression'-Function
[20250404_044324.]: 'cubic_regression': minmax = FALSE
[20250404_044324.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_044324.]: Logging df_agg: CpG#2
[20250404_044324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044324.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_044324.]: Entered 'hyperbolic_regression'-Function
[20250404_044324.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044324.]: Entered 'cubic_regression'-Function
[20250404_044324.]: 'cubic_regression': minmax = FALSE
[20250404_044324.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_044324.]: Logging df_agg: CpG#3
[20250404_044324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044324.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_044324.]: Entered 'hyperbolic_regression'-Function
[20250404_044324.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_044325.]: Logging df_agg: CpG#4
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_044325.]: Logging df_agg: CpG#5
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044324.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_044324.]: Logging df_agg: CpG#6
[20250404_044324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044324.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_044324.]: Entered 'hyperbolic_regression'-Function
[20250404_044324.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_044325.]: Logging df_agg: CpG#7
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_044325.]: Logging df_agg: CpG#8
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_044325.]: Logging df_agg: CpG#9
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_044326.]: Logging df_agg: row_means
[20250404_044326.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044326.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_044326.]: Entered 'hyperbolic_regression'-Function
[20250404_044326.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044326.]: Entered 'cubic_regression'-Function
[20250404_044326.]: 'cubic_regression': minmax = FALSE
[20250404_044327.]: Entered 'solving_equations'-Function
[20250404_044327.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 79.8673456895745
[20250404_044327.]: Samplename: Sample#1
Root: 79.867
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.7900184340805
[20250404_044327.]: Samplename: Sample#10
Root: 29.79
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.6525415639691
[20250404_044327.]: Samplename: Sample#2
Root: 41.653
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.4652090254513
[20250404_044327.]: Samplename: Sample#3
Root: 57.465
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.2007130627765
[20250404_044327.]: Samplename: Sample#4
Root: 9.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8059600538131
[20250404_044327.]: Samplename: Sample#5
Root: 21.806
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.083796735881
[20250404_044327.]: Samplename: Sample#6
Root: 23.084
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.5034245569385
[20250404_044327.]: Samplename: Sample#7
Root: 45.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6987904075704
[20250404_044327.]: Samplename: Sample#8
Root: 85.699
--> Root in between the borders! Added to results.
Hyperbolic solved: -3.66512807265101
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -3.665
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Solving cubic regression for CpG#2
Coefficients: -60.0166632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#1
Root: 76.388
--> Root in between the borders! Added to results.
Coefficients: -19.33132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#10
Root: 31.437
--> Root in between the borders! Added to results.
Coefficients: -28.1616632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#2
Root: 42.956
--> Root in between the borders! Added to results.
Coefficients: -42.07832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#3
Root: 58.838
--> Root in between the borders! Added to results.
Coefficients: -2.49332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#4
Root: 4.715
--> Root in between the borders! Added to results.
Coefficients: -11.94832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#5
Root: 20.644
--> Root in between the borders! Added to results.
Coefficients: -10.36332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#6
Root: 18.159
--> Root in between the borders! Added to results.
Coefficients: -26.77132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#7
Root: 41.228
--> Root in between the borders! Added to results.
Coefficients: -70.81532996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#8
Root: 85.785
--> Root in between the borders! Added to results.
Coefficients: -1.41332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#9
Root: 2.703
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.9349254100163
[20250404_044327.]: Samplename: Sample#1
Root: 74.935
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.6844381581493
[20250404_044327.]: Samplename: Sample#10
Root: 27.684
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.852019114379
[20250404_044327.]: Samplename: Sample#2
Root: 41.852
--> Root in between the borders! Added to results.
Hyperbolic solved: 55.8325180209418
[20250404_044327.]: Samplename: Sample#3
Root: 55.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.03519251633153
[20250404_044327.]: Samplename: Sample#4
Root: 8.035
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.1066315721853
[20250404_044327.]: Samplename: Sample#5
Root: 24.107
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2419820027673
[20250404_044327.]: Samplename: Sample#6
Root: 26.242
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0944922703422
[20250404_044327.]: Samplename: Sample#7
Root: 44.094
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.8279382585787
[20250404_044327.]: Samplename: Sample#8
Root: 85.828
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.666482392725758
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.666
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 76.3495278640236
[20250404_044327.]: Samplename: Sample#1
Root: 76.35
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.2568553570941
[20250404_044327.]: Samplename: Sample#10
Root: 28.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4089839390807
[20250404_044327.]: Samplename: Sample#2
Root: 43.409
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.5435236860146
[20250404_044327.]: Samplename: Sample#3
Root: 58.544
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.3087045690571
[20250404_044327.]: Samplename: Sample#4
Root: 10.309
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.183045165659
[20250404_044327.]: Samplename: Sample#5
Root: 22.183
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.1337769553499
[20250404_044327.]: Samplename: Sample#6
Root: 27.134
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8321096080155
[20250404_044327.]: Samplename: Sample#7
Root: 41.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6890189074743
[20250404_044327.]: Samplename: Sample#8
Root: 85.689
--> Root in between the borders! Added to results.
Hyperbolic solved: 2.42232098177269
[20250404_044327.]: Samplename: Sample#9
Root: 2.422
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving cubic regression for CpG#5
Coefficients: -48.4612946127946Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -14.2119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#10
Root: 27.256
--> Root in between the borders! Added to results.
Coefficients: -25.9451041366041Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#2
Root: 44.648
--> Root in between the borders! Added to results.
Coefficients: -32.6879612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#3
Root: 53.538
--> Root in between the borders! Added to results.
Coefficients: -4.69796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#4
Root: 10.206
--> Root in between the borders! Added to results.
Coefficients: -12.0579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#5
Root: 23.695
--> Root in between the borders! Added to results.
Coefficients: -13.9179612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#6
Root: 26.778
--> Root in between the borders! Added to results.
Coefficients: -24.9119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#7
Root: 43.226
--> Root in between the borders! Added to results.
Coefficients: -63.7579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#8
Root: 88.581
--> Root in between the borders! Added to results.
Coefficients: -0.587961279461277Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#9
Root: 1.375
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2780593622711
[20250404_044327.]: Samplename: Sample#1
Root: 79.278
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2012458984074
[20250404_044327.]: Samplename: Sample#10
Root: 30.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8474393624107
[20250404_044327.]: Samplename: Sample#2
Root: 41.847
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8423517321508
[20250404_044327.]: Samplename: Sample#3
Root: 56.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.87856046118588
[20250404_044327.]: Samplename: Sample#4
Root: 8.879
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.69015950004
[20250404_044327.]: Samplename: Sample#5
Root: 18.69
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9309263534749
[20250404_044327.]: Samplename: Sample#6
Root: 29.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8148560027697
[20250404_044327.]: Samplename: Sample#7
Root: 42.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.7501831416152
[20250404_044327.]: Samplename: Sample#8
Root: 86.75
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.51516194985267
[20250404_044327.]: Samplename: Sample#9
Root: 1.515
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 78.2565592569279
[20250404_044327.]: Samplename: Sample#1
Root: 78.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.488739349283
[20250404_044327.]: Samplename: Sample#10
Root: 25.489
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.3712258915285
[20250404_044327.]: Samplename: Sample#2
Root: 47.371
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.3142673189298
[20250404_044327.]: Samplename: Sample#3
Root: 58.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7212231360573
[20250404_044327.]: Samplename: Sample#4
Root: 11.721
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.3797485992238
[20250404_044327.]: Samplename: Sample#5
Root: 25.38
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.4095133062523
[20250404_044327.]: Samplename: Sample#6
Root: 29.41
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.5755071469546
[20250404_044327.]: Samplename: Sample#7
Root: 44.576
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.9628731021447
[20250404_044327.]: Samplename: Sample#8
Root: 85.963
--> Root in between the borders! Added to results.
Hyperbolic solved: -4.1645647175353
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.165
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Solving cubic regression for CpG#8
Coefficients: -56.4535185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#1
Root: 72.337
--> Root in between the borders! Added to results.
Coefficients: -18.6701851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#10
Root: 28.678
--> Root in between the borders! Added to results.
Coefficients: -24.0387566137566Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#2
Root: 35.595
--> Root in between the borders! Added to results.
Coefficients: -43.9451851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#3
Root: 58.861
--> Root in between the borders! Added to results.
Coefficients: -5.70018518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#4
Root: 9.868
--> Root in between the borders! Added to results.
Coefficients: -12.4851851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#5
Root: 20.166
--> Root in between the borders! Added to results.
Coefficients: -26.8801851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#6
Root: 39.117
--> Root in between the borders! Added to results.
Coefficients: -31.8421851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#7
Root: 45.08
--> Root in between the borders! Added to results.
Coefficients: -68.0081851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#8
Root: 84.373
--> Root in between the borders! Added to results.
Coefficients: 2.07981481481482Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.026
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Solving cubic regression for CpG#9
Coefficients: -60.8091986531987Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#1
Root: 81.262
--> Root in between the borders! Added to results.
Coefficients: -14.5538653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#10
Root: 24.569
--> Root in between the borders! Added to results.
Coefficients: -26.6344367484368Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#2
Root: 45.035
--> Root in between the borders! Added to results.
Coefficients: -35.4783653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#3
Root: 57.113
--> Root in between the borders! Added to results.
Coefficients: -4.73586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#4
Root: 7.362
--> Root in between the borders! Added to results.
Coefficients: -12.5308653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#5
Root: 20.907
--> Root in between the borders! Added to results.
Coefficients: -21.9358653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#6
Root: 37.545
--> Root in between the borders! Added to results.
Coefficients: -25.1998653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#7
Root: 42.828
--> Root in between the borders! Added to results.
Coefficients: -70.5118653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#8
Root: 88.082
--> Root in between the borders! Added to results.
Coefficients: -0.505865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#9
Root: 0.749
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 77.0692797356261
[20250404_044327.]: Samplename: Sample#1
Root: 77.069
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3620040447844
[20250404_044327.]: Samplename: Sample#10
Root: 28.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.5026170660315
[20250404_044327.]: Samplename: Sample#2
Root: 42.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.2972045344154
[20250404_044327.]: Samplename: Sample#3
Root: 57.297
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.82704040274281
[20250404_044327.]: Samplename: Sample#4
Root: 8.827
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8102591233667
[20250404_044327.]: Samplename: Sample#5
Root: 21.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.722865717687
[20250404_044327.]: Samplename: Sample#6
Root: 28.723
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4105098027891
[20250404_044327.]: Samplename: Sample#7
Root: 43.411
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.4143551699061
[20250404_044327.]: Samplename: Sample#8
Root: 86.414
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.237019926848022
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.237
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Entered 'solving_equations'-Function
[20250404_044327.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_044327.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_044327.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_044327.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_044327.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_044327.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_044327.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_044327.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_044327.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_044327.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_044327.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_044327.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_044327.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_044327.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_044327.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_044327.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_044327.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_044327.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_044327.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_044327.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_044327.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_044327.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_044327.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_044327.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_044327.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_044328.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_044328.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_044328.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_044328.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_044328.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_044328.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_044328.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_044328.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_044328.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_044328.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_044328.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_044328.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_044328.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_044328.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_044328.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_044328.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_044328.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_044328.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_044328.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_044328.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_044328.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_044328.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_044328.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_044328.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_044328.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_044328.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_044328.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_044328.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_044328.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_044329.]: Entered 'clean_dt'-Function
[20250404_044329.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044329.]: got experimental data
[20250404_044329.]: Entered 'clean_dt'-Function
[20250404_044329.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044329.]: got calibration data
[20250404_044329.]: ### Starting with regression calculations ###
[20250404_044329.]: Entered 'regression_type1'-Function
[20250404_044330.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044330.]: Logging df_agg: CpG#1
[20250404_044330.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044330.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044330.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044330.]: Entered 'hyperbolic_regression'-Function
[20250404_044330.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044330.]: Entered 'cubic_regression'-Function
[20250404_044330.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044330.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044330.]: Logging df_agg: CpG#2
[20250404_044330.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044330.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044330.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044330.]: Entered 'hyperbolic_regression'-Function
[20250404_044330.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044331.]: Entered 'cubic_regression'-Function
[20250404_044331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044331.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044331.]: Logging df_agg: CpG#3
[20250404_044331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044331.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044331.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044331.]: Entered 'hyperbolic_regression'-Function
[20250404_044331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: Entered 'cubic_regression'-Function
[20250404_044332.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044332.]: Logging df_agg: CpG#4
[20250404_044332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044332.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044332.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044332.]: Entered 'hyperbolic_regression'-Function
[20250404_044332.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: Entered 'cubic_regression'-Function
[20250404_044332.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044332.]: Logging df_agg: CpG#5
[20250404_044332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044332.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044332.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044332.]: Entered 'hyperbolic_regression'-Function
[20250404_044332.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044333.]: Entered 'cubic_regression'-Function
[20250404_044333.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044331.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044331.]: Logging df_agg: CpG#6
[20250404_044331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044331.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044331.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044331.]: Entered 'hyperbolic_regression'-Function
[20250404_044331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044331.]: Entered 'cubic_regression'-Function
[20250404_044331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044332.]: Logging df_agg: CpG#7
[20250404_044332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044332.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044332.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044332.]: Entered 'hyperbolic_regression'-Function
[20250404_044332.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: Entered 'cubic_regression'-Function
[20250404_044332.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044332.]: Logging df_agg: CpG#8
[20250404_044332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044332.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044332.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044332.]: Entered 'hyperbolic_regression'-Function
[20250404_044332.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: Entered 'cubic_regression'-Function
[20250404_044332.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044333.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044333.]: Logging df_agg: CpG#9
[20250404_044333.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044333.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044333.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044333.]: Entered 'hyperbolic_regression'-Function
[20250404_044333.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044333.]: Entered 'cubic_regression'-Function
[20250404_044333.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044333.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044333.]: Logging df_agg: row_means
[20250404_044333.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044333.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044333.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044333.]: Entered 'hyperbolic_regression'-Function
[20250404_044333.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044334.]: Entered 'cubic_regression'-Function
[20250404_044334.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044337.]: Entered 'regression_type1'-Function
[20250404_044338.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044338.]: Logging df_agg: CpG#1
[20250404_044338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044338.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044338.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044338.]: Entered 'hyperbolic_regression'-Function
[20250404_044338.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: Entered 'cubic_regression'-Function
[20250404_044339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044339.]: Logging df_agg: CpG#2
[20250404_044339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044339.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044339.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044339.]: Entered 'hyperbolic_regression'-Function
[20250404_044339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: Entered 'cubic_regression'-Function
[20250404_044339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044339.]: Logging df_agg: CpG#3
[20250404_044339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044339.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044339.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044339.]: Entered 'hyperbolic_regression'-Function
[20250404_044339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: Entered 'cubic_regression'-Function
[20250404_044340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044340.]: Logging df_agg: CpG#4
[20250404_044340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044340.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044340.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044340.]: Entered 'hyperbolic_regression'-Function
[20250404_044340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: Entered 'cubic_regression'-Function
[20250404_044340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044341.]: Logging df_agg: CpG#5
[20250404_044341.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044341.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044341.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044341.]: Entered 'hyperbolic_regression'-Function
[20250404_044341.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: Entered 'cubic_regression'-Function
[20250404_044341.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044338.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044339.]: Logging df_agg: CpG#6
[20250404_044339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044339.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044339.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044339.]: Entered 'hyperbolic_regression'-Function
[20250404_044339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: Entered 'cubic_regression'-Function
[20250404_044339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044340.]: Logging df_agg: CpG#7
[20250404_044340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044340.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044340.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044340.]: Entered 'hyperbolic_regression'-Function
[20250404_044340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: Entered 'cubic_regression'-Function
[20250404_044340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044340.]: Logging df_agg: CpG#8
[20250404_044340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044340.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044340.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044340.]: Entered 'hyperbolic_regression'-Function
[20250404_044340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: Entered 'cubic_regression'-Function
[20250404_044341.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044341.]: Logging df_agg: CpG#9
[20250404_044341.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044341.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044341.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044341.]: Entered 'hyperbolic_regression'-Function
[20250404_044341.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: Entered 'cubic_regression'-Function
[20250404_044341.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044342.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044342.]: Logging df_agg: row_means
[20250404_044342.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044342.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044342.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044342.]: Entered 'hyperbolic_regression'-Function
[20250404_044342.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044342.]: Entered 'cubic_regression'-Function
[20250404_044342.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044345.]: Entered 'clean_dt'-Function
[20250404_044345.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044345.]: got experimental data
[20250404_044345.]: Entered 'clean_dt'-Function
[20250404_044345.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044345.]: got calibration data
[20250404_044345.]: ### Starting with regression calculations ###
[20250404_044345.]: Entered 'regression_type1'-Function
[20250404_044345.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044345.]: Logging df_agg: CpG#1
[20250404_044345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044345.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044345.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044345.]: Entered 'hyperbolic_regression'-Function
[20250404_044345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044346.]: Entered 'cubic_regression'-Function
[20250404_044346.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044346.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044346.]: Logging df_agg: CpG#2
[20250404_044346.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044346.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044346.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044346.]: Entered 'hyperbolic_regression'-Function
[20250404_044346.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: Entered 'cubic_regression'-Function
[20250404_044347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044347.]: Logging df_agg: CpG#3
[20250404_044347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044347.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044347.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044347.]: Entered 'hyperbolic_regression'-Function
[20250404_044347.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: Entered 'cubic_regression'-Function
[20250404_044348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044348.]: Logging df_agg: CpG#4
[20250404_044348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044348.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044348.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044348.]: Entered 'hyperbolic_regression'-Function
[20250404_044348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: Entered 'cubic_regression'-Function
[20250404_044348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044348.]: Logging df_agg: CpG#5
[20250404_044348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044348.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044348.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044348.]: Entered 'hyperbolic_regression'-Function
[20250404_044348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044349.]: Entered 'cubic_regression'-Function
[20250404_044349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044346.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044346.]: Logging df_agg: CpG#6
[20250404_044346.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044346.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044346.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044346.]: Entered 'hyperbolic_regression'-Function
[20250404_044346.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: Entered 'cubic_regression'-Function
[20250404_044347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044347.]: Logging df_agg: CpG#7
[20250404_044347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044347.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044347.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044347.]: Entered 'hyperbolic_regression'-Function
[20250404_044347.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: Entered 'cubic_regression'-Function
[20250404_044347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044348.]: Logging df_agg: CpG#8
[20250404_044348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044348.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044348.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044348.]: Entered 'hyperbolic_regression'-Function
[20250404_044348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: Entered 'cubic_regression'-Function
[20250404_044348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044348.]: Logging df_agg: CpG#9
[20250404_044348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044348.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044348.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044348.]: Entered 'hyperbolic_regression'-Function
[20250404_044348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044349.]: Entered 'cubic_regression'-Function
[20250404_044349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044349.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044349.]: Logging df_agg: row_means
[20250404_044349.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044349.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044349.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044349.]: Entered 'hyperbolic_regression'-Function
[20250404_044349.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044349.]: Entered 'cubic_regression'-Function
[20250404_044349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044352.]: Entered 'regression_type1'-Function
[20250404_044353.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044354.]: Logging df_agg: CpG#1
[20250404_044354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044354.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044354.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044354.]: Entered 'hyperbolic_regression'-Function
[20250404_044354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044354.]: Entered 'cubic_regression'-Function
[20250404_044354.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044354.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044354.]: Logging df_agg: CpG#2
[20250404_044354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044354.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044354.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044354.]: Entered 'hyperbolic_regression'-Function
[20250404_044354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: Entered 'cubic_regression'-Function
[20250404_044355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044355.]: Logging df_agg: CpG#3
[20250404_044355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044355.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044355.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044355.]: Entered 'hyperbolic_regression'-Function
[20250404_044355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: Entered 'cubic_regression'-Function
[20250404_044355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044356.]: Logging df_agg: CpG#4
[20250404_044356.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044356.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044356.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044356.]: Entered 'hyperbolic_regression'-Function
[20250404_044356.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: Entered 'cubic_regression'-Function
[20250404_044356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044356.]: Logging df_agg: CpG#5
[20250404_044356.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044356.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044356.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044356.]: Entered 'hyperbolic_regression'-Function
[20250404_044356.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: Entered 'cubic_regression'-Function
[20250404_044356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044354.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044354.]: Logging df_agg: CpG#6
[20250404_044354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044354.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044354.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044354.]: Entered 'hyperbolic_regression'-Function
[20250404_044354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: Entered 'cubic_regression'-Function
[20250404_044355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044355.]: Logging df_agg: CpG#7
[20250404_044355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044355.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044355.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044355.]: Entered 'hyperbolic_regression'-Function
[20250404_044355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: Entered 'cubic_regression'-Function
[20250404_044355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044355.]: Logging df_agg: CpG#8
[20250404_044355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044355.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044355.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044355.]: Entered 'hyperbolic_regression'-Function
[20250404_044355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: Entered 'cubic_regression'-Function
[20250404_044356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044356.]: Logging df_agg: CpG#9
[20250404_044356.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044356.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044356.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044356.]: Entered 'hyperbolic_regression'-Function
[20250404_044356.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: Entered 'cubic_regression'-Function
[20250404_044356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044357.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044357.]: Logging df_agg: row_means
[20250404_044357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044357.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044357.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044357.]: Entered 'hyperbolic_regression'-Function
[20250404_044357.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044357.]: Entered 'cubic_regression'-Function
[20250404_044357.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044358.]: Entered 'solving_equations'-Function
[20250404_044358.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_044358.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_044358.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_044358.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_044358.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_044358.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_044358.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_044358.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_044358.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044358.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044358.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 0
[20250404_044358.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7851657015183
[20250404_044358.]: Samplename: 12.5
Root: 10.785
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.0727152156421
[20250404_044358.]: Samplename: 25
Root: 26.073
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.2074258210424
[20250404_044358.]: Samplename: 37.5
Root: 35.207
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.9305924748583
[20250404_044358.]: Samplename: 50
Root: 47.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.2847555363015
[20250404_044358.]: Samplename: 62.5
Root: 67.285
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.735332403378
[20250404_044359.]: Samplename: 75
Root: 75.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.1313047876192
[20250404_044359.]: Samplename: 87.5
Root: 84.131
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_044359.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_044359.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_044359.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_044359.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_044359.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_044359.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_044359.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_044359.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_044359.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_044359.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_044359.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_044359.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_044359.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_044359.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.36665146544904
[20250404_044359.]: Samplename: 12.5
Root: 8.367
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.0855280383989
[20250404_044359.]: Samplename: 25
Root: 23.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0098400819818
[20250404_044359.]: Samplename: 37.5
Root: 37.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.0085868408378
[20250404_044359.]: Samplename: 50
Root: 51.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.7441416833696
[20250404_044359.]: Samplename: 62.5
Root: 62.744
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.6857826005162
[20250404_044359.]: Samplename: 75
Root: 76.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.3046084696663
[20250404_044359.]: Samplename: 87.5
Root: 86.305
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_044359.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_044359.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_044359.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_044359.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_044359.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_044359.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_044359.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_044359.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_044359.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_044359.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_044359.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_044359.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_044359.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_044359.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.27736114274885
[20250404_044359.]: Samplename: 12.5
Root: 7.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.9863834890886
[20250404_044359.]: Samplename: 25
Root: 24.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0400823094579
[20250404_044359.]: Samplename: 37.5
Root: 34.04
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3077192847199
[20250404_044359.]: Samplename: 50
Root: 52.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0861558866387
[20250404_044359.]: Samplename: 62.5
Root: 65.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.3136588178128
[20250404_044359.]: Samplename: 75
Root: 78.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.058248740059
[20250404_044359.]: Samplename: 87.5
Root: 81.058
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_044359.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_044359.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_044359.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_044359.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_044359.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_044359.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_044359.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_044359.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_044359.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_044359.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_044359.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_044359.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_044359.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_044359.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: ### Starting with regression calculations ###
[20250404_044359.]: Entered 'regression_type1'-Function
[20250404_044400.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_044400.]: Logging df_agg: CpG#1
[20250404_044400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044400.]: c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_044400.]: Entered 'hyperbolic_regression'-Function
[20250404_044400.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044401.]: Entered 'cubic_regression'-Function
[20250404_044401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044401.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_044401.]: Logging df_agg: CpG#2
[20250404_044401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044401.]: c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_044401.]: Entered 'hyperbolic_regression'-Function
[20250404_044401.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044401.]: Entered 'cubic_regression'-Function
[20250404_044401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_044402.]: Logging df_agg: CpG#3
[20250404_044402.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044402.]: c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_044402.]: Entered 'hyperbolic_regression'-Function
[20250404_044402.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: Entered 'cubic_regression'-Function
[20250404_044402.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_044402.]: Logging df_agg: CpG#4
[20250404_044402.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044402.]: c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_044402.]: Entered 'hyperbolic_regression'-Function
[20250404_044402.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: Entered 'cubic_regression'-Function
[20250404_044403.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_044403.]: Logging df_agg: CpG#5
[20250404_044403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044403.]: c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_044403.]: Entered 'hyperbolic_regression'-Function
[20250404_044403.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: Entered 'cubic_regression'-Function
[20250404_044403.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044401.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_044401.]: Logging df_agg: CpG#6
[20250404_044401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044401.]: c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_044401.]: Entered 'hyperbolic_regression'-Function
[20250404_044401.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: Entered 'cubic_regression'-Function
[20250404_044402.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_044402.]: Logging df_agg: CpG#7
[20250404_044402.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044402.]: c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_044402.]: Entered 'hyperbolic_regression'-Function
[20250404_044402.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: Entered 'cubic_regression'-Function
[20250404_044402.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_044403.]: Logging df_agg: CpG#8
[20250404_044403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044403.]: c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_044403.]: Entered 'hyperbolic_regression'-Function
[20250404_044403.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: Entered 'cubic_regression'-Function
[20250404_044403.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_044403.]: Logging df_agg: CpG#9
[20250404_044403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044403.]: c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_044403.]: Entered 'hyperbolic_regression'-Function
[20250404_044403.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044404.]: Entered 'cubic_regression'-Function
[20250404_044404.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044404.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_044404.]: Logging df_agg: row_means
[20250404_044404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044404.]: c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_044404.]: Entered 'hyperbolic_regression'-Function
[20250404_044404.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044404.]: Entered 'cubic_regression'-Function
[20250404_044404.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044405.]: Entered 'solving_equations'-Function
[20250404_044405.]: Solving cubic regression for CpG#1
Coefficients: 0Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.30533333333333Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 12.5
Root: 10.279
--> Root in between the borders! Added to results.
Coefficients: -14.352Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 25
Root: 21.591
--> Root in between the borders! Added to results.
Coefficients: -23.244Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 37.5
Root: 36.617
--> Root in between the borders! Added to results.
Coefficients: -33.8645Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 50
Root: 52.729
--> Root in between the borders! Added to results.
Coefficients: -45.318Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 62.5
Root: 66.532
--> Root in between the borders! Added to results.
Coefficients: -54.857Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 75
Root: 75.773
--> Root in between the borders! Added to results.
Coefficients: -62.062Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 87.5
Root: 81.772
--> Root in between the borders! Added to results.
Coefficients: -90.01Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#3
Coefficients: 0Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.67Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 12.5
Root: 9.387
--> Root in between the borders! Added to results.
Coefficients: -14.526Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 25
Root: 24.373
--> Root in between the borders! Added to results.
Coefficients: -21.71Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 37.5
Root: 36.135
--> Root in between the borders! Added to results.
Coefficients: -31.8725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 50
Root: 51.29
--> Root in between the borders! Added to results.
Coefficients: -42.986Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 62.5
Root: 65.561
--> Root in between the borders! Added to results.
Coefficients: -54.0725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 75
Root: 77.683
--> Root in between the borders! Added to results.
Coefficients: -56.7533333333333Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 87.5
Root: 80.348
--> Root in between the borders! Added to results.
Coefficients: -79.762Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#4
Coefficients: 0Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.65533333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 12.5
Root: 11.333
--> Root in between the borders! Added to results.
Coefficients: -15.206Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 25
Root: 22.933
--> Root in between the borders! Added to results.
Coefficients: -24.93Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 37.5
Root: 37.542
--> Root in between the borders! Added to results.
Coefficients: -33.0395Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 50
Root: 48.772
--> Root in between the borders! Added to results.
Coefficients: -49.658Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 62.5
Root: 68.324
--> Root in between the borders! Added to results.
Coefficients: -55.942Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 75
Root: 74.614
--> Root in between the borders! Added to results.
Coefficients: -64.9953333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 87.5
Root: 82.816
--> Root in between the borders! Added to results.
Coefficients: -87.724Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_044405.]: Solving cubic regression for CpG#6
Coefficients: 0Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.54266666666667Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 12.5
Root: 11.495
--> Root in between the borders! Added to results.
Coefficients: -15.692Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 25
Root: 26.346
--> Root in between the borders! Added to results.
Coefficients: -21.804Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 37.5
Root: 35.332
--> Root in between the borders! Added to results.
Coefficients: -33.2485Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 50
Root: 50.228
--> Root in between the borders! Added to results.
Coefficients: -46.704Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 62.5
Root: 65.055
--> Root in between the borders! Added to results.
Coefficients: -55.636Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 75
Root: 73.641
--> Root in between the borders! Added to results.
Coefficients: -71.3493333333333Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 87.5
Root: 86.903
--> Root in between the borders! Added to results.
Coefficients: -89.46Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#7
Coefficients: 0Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.18066666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 12.5
Root: 8.108
--> Root in between the borders! Added to results.
Coefficients: -10.05Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 25
Root: 21.288
--> Root in between the borders! Added to results.
Coefficients: -16.236Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 37.5
Root: 36.173
--> Root in between the borders! Added to results.
Coefficients: -24.8165Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 50
Root: 54.247
--> Root in between the borders! Added to results.
Coefficients: -32.75Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 62.5
Root: 67.087
--> Root in between the borders! Added to results.
Coefficients: -39.954Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 75
Root: 76.377
--> Root in between the borders! Added to results.
Coefficients: -42.9206666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 87.5
Root: 79.728
--> Root in between the borders! Added to results.
Coefficients: -66.008Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_044406.]: Solving cubic regression for CpG#9
Coefficients: 0Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 12.5
Root: 8.93
--> Root in between the borders! Added to results.
Coefficients: -13.716Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 25
Root: 24.492
--> Root in between the borders! Added to results.
Coefficients: -19.634Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 37.5
Root: 35.53
--> Root in between the borders! Added to results.
Coefficients: -30.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 50
Root: 52.349
--> Root in between the borders! Added to results.
Coefficients: -41.696Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 62.5
Root: 65.528
--> Root in between the borders! Added to results.
Coefficients: -51.9135Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 75
Root: 74.87
--> Root in between the borders! Added to results.
Coefficients: -64.5026666666667Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 87.5
Root: 84.256
--> Root in between the borders! Added to results.
Coefficients: -92Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044406.]: Solving cubic regression for row_means
Coefficients: 0Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.70125925925926Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 12.5
Root: 9.866
--> Root in between the borders! Added to results.
Coefficients: -14.126Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 25
Root: 24.413
--> Root in between the borders! Added to results.
Coefficients: -21.378Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 37.5
Root: 36.177
--> Root in between the borders! Added to results.
Coefficients: -31.7547777777778Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 50
Root: 51.091
--> Root in between the borders! Added to results.
Coefficients: -43.9506666666667Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 62.5
Root: 65.785
--> Root in between the borders! Added to results.
Coefficients: -53.632Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 75
Root: 75.683
--> Root in between the borders! Added to results.
Coefficients: -61.6601481481482Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 87.5
Root: 82.966
--> Root in between the borders! Added to results.
Coefficients: -83.9631111111111Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044406.]: ### Starting with regression calculations ###
[20250404_044406.]: Entered 'regression_type1'-Function
[20250404_044407.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_044407.]: Logging df_agg: CpG#1
[20250404_044407.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044407.]: c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_044407.]: Entered 'hyperbolic_regression'-Function
[20250404_044407.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044407.]: Entered 'cubic_regression'-Function
[20250404_044407.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044408.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_044408.]: Logging df_agg: CpG#2
[20250404_044408.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044408.]: c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_044408.]: Entered 'hyperbolic_regression'-Function
[20250404_044408.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044408.]: Entered 'cubic_regression'-Function
[20250404_044408.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044408.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_044408.]: Logging df_agg: CpG#3
[20250404_044408.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044408.]: c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_044408.]: Entered 'hyperbolic_regression'-Function
[20250404_044408.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: Entered 'cubic_regression'-Function
[20250404_044409.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_044409.]: Logging df_agg: CpG#4
[20250404_044409.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044409.]: c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_044409.]: Entered 'hyperbolic_regression'-Function
[20250404_044409.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: Entered 'cubic_regression'-Function
[20250404_044409.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_044409.]: Logging df_agg: CpG#5
[20250404_044409.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044409.]: c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_044409.]: Entered 'hyperbolic_regression'-Function
[20250404_044409.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: Entered 'cubic_regression'-Function
[20250404_044410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044407.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_044408.]: Logging df_agg: CpG#6
[20250404_044408.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044408.]: c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_044408.]: Entered 'hyperbolic_regression'-Function
[20250404_044408.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044408.]: Entered 'cubic_regression'-Function
[20250404_044408.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_044409.]: Logging df_agg: CpG#7
[20250404_044409.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044409.]: c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_044409.]: Entered 'hyperbolic_regression'-Function
[20250404_044409.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: Entered 'cubic_regression'-Function
[20250404_044409.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_044409.]: Logging df_agg: CpG#8
[20250404_044409.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044409.]: c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_044409.]: Entered 'hyperbolic_regression'-Function
[20250404_044409.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: Entered 'cubic_regression'-Function
[20250404_044410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_044410.]: Logging df_agg: CpG#9
[20250404_044410.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044410.]: c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_044410.]: Entered 'hyperbolic_regression'-Function
[20250404_044410.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: Entered 'cubic_regression'-Function
[20250404_044410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_044410.]: Logging df_agg: row_means
[20250404_044410.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044410.]: c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_044410.]: Entered 'hyperbolic_regression'-Function
[20250404_044410.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044411.]: Entered 'cubic_regression'-Function
[20250404_044411.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044412.]: Entered 'solving_equations'-Function
[20250404_044412.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 78.9856894800976
[20250404_044412.]: Samplename: Sample#1
Root: 78.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 31.2695317984092
[20250404_044412.]: Samplename: Sample#10
Root: 31.27
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.7015782380441
[20250404_044412.]: Samplename: Sample#2
Root: 42.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.8152127901709
[20250404_044412.]: Samplename: Sample#3
Root: 57.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.2334360674289
[20250404_044412.]: Samplename: Sample#4
Root: 11.233
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.5293831001518
[20250404_044412.]: Samplename: Sample#5
Root: 23.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7706743072545
[20250404_044412.]: Samplename: Sample#6
Root: 24.771
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.3953425213349
[20250404_044412.]: Samplename: Sample#7
Root: 46.395
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.45071436915
[20250404_044412.]: Samplename: Sample#8
Root: 84.451
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.41337105576252
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.413
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving cubic regression for CpG#2
Coefficients: -59.7333333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#1
Root: 76.346
--> Root in between the borders! Added to results.
Coefficients: -19.048Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#10
Root: 31.371
--> Root in between the borders! Added to results.
Coefficients: -27.8783333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#2
Root: 43.142
--> Root in between the borders! Added to results.
Coefficients: -41.795Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#3
Root: 59.121
--> Root in between the borders! Added to results.
Coefficients: -2.21Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#4
Root: 4.128
--> Root in between the borders! Added to results.
Coefficients: -11.665Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#5
Root: 20.292
--> Root in between the borders! Added to results.
Coefficients: -10.08Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#6
Root: 17.745
--> Root in between the borders! Added to results.
Coefficients: -26.488Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#7
Root: 41.383
--> Root in between the borders! Added to results.
Coefficients: -70.532Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#8
Root: 85.378
--> Root in between the borders! Added to results.
Coefficients: -1.13Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#9
Root: 2.127
--> Root in between the borders! Added to results.
[20250404_044412.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.5474014641742
[20250404_044412.]: Samplename: Sample#1
Root: 74.547
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3579002775045
[20250404_044412.]: Samplename: Sample#10
Root: 28.358
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.6085496577593
[20250404_044412.]: Samplename: Sample#2
Root: 42.609
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.3286114696456
[20250404_044412.]: Samplename: Sample#3
Root: 56.329
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.99034441243248
[20250404_044412.]: Samplename: Sample#4
Root: 7.99
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7023143744962
[20250404_044412.]: Samplename: Sample#5
Root: 24.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.8868798900698
[20250404_044412.]: Samplename: Sample#6
Root: 26.887
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.8318233973603
[20250404_044412.]: Samplename: Sample#7
Root: 44.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6737871528405
[20250404_044412.]: Samplename: Sample#8
Root: 84.674
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.26200732612128
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.262
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 75.8433680333876
[20250404_044412.]: Samplename: Sample#1
Root: 75.843
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.0603248948201
[20250404_044412.]: Samplename: Sample#10
Root: 29.06
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0355928114108
[20250404_044412.]: Samplename: Sample#2
Root: 44.036
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.7751115686327
[20250404_044412.]: Samplename: Sample#3
Root: 58.775
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0319154866029
[20250404_044412.]: Samplename: Sample#4
Root: 11.032
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.9948971650737
[20250404_044412.]: Samplename: Sample#5
Root: 22.995
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.9415139419957
[20250404_044412.]: Samplename: Sample#6
Root: 27.942
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.4874049425657
[20250404_044412.]: Samplename: Sample#7
Root: 42.487
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6802730343613
[20250404_044412.]: Samplename: Sample#8
Root: 84.68
--> Root in between the borders! Added to results.
Hyperbolic solved: 3.00887785677921
[20250404_044412.]: Samplename: Sample#9
Root: 3.009
--> Root in between the borders! Added to results.
[20250404_044412.]: Solving cubic regression for CpG#5
Coefficients: -47.8373333333333Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -13.588Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#10
Root: 27.212
--> Root in between the borders! Added to results.
Coefficients: -25.3211428571429Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#2
Root: 44.85
--> Root in between the borders! Added to results.
Coefficients: -32.064Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#3
Root: 53.741
--> Root in between the borders! Added to results.
Coefficients: -4.074Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#4
Root: 9.444
--> Root in between the borders! Added to results.
Coefficients: -11.434Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#5
Root: 23.55
--> Root in between the borders! Added to results.
Coefficients: -13.294Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#6
Root: 26.722
--> Root in between the borders! Added to results.
Coefficients: -24.288Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#7
Root: 43.42
--> Root in between the borders! Added to results.
Coefficients: -63.134Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#8
Root: 88.215
--> Root in between the borders! Added to results.
Coefficients: 0.0360000000000005Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.091
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2200555510382
[20250404_044412.]: Samplename: Sample#1
Root: 79.22
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2526528381147
[20250404_044412.]: Samplename: Sample#10
Root: 30.253
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.9196854329573
[20250404_044412.]: Samplename: Sample#2
Root: 41.92
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8984354098215
[20250404_044412.]: Samplename: Sample#3
Root: 56.898
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.81576403111374
[20250404_044412.]: Samplename: Sample#4
Root: 8.816
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.6921622783918
[20250404_044412.]: Samplename: Sample#5
Root: 18.692
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9815019073132
[20250404_044412.]: Samplename: Sample#6
Root: 29.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8875178508205
[20250404_044412.]: Samplename: Sample#7
Root: 42.888
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.6303733181195
[20250404_044412.]: Samplename: Sample#8
Root: 86.63
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.38997712955107
[20250404_044412.]: Samplename: Sample#9
Root: 1.39
--> Root in between the borders! Added to results.
[20250404_044412.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 77.5278331978133
[20250404_044412.]: Samplename: Sample#1
Root: 77.528
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.0895401031897
[20250404_044412.]: Samplename: Sample#10
Root: 27.09
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.4382794903846
[20250404_044412.]: Samplename: Sample#2
Root: 48.438
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.8815971416453
[20250404_044412.]: Samplename: Sample#3
Root: 58.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.3295768294236
[20250404_044412.]: Samplename: Sample#4
Root: 13.33
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9816196357542
[20250404_044412.]: Samplename: Sample#5
Root: 26.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.9612159665911
[20250404_044412.]: Samplename: Sample#6
Root: 30.961
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.7456547820365
[20250404_044412.]: Samplename: Sample#7
Root: 45.746
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6033538318025
[20250404_044412.]: Samplename: Sample#8
Root: 84.603
--> Root in between the borders! Added to results.
Hyperbolic solved: -2.87380061592101
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.874
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving cubic regression for CpG#8
Coefficients: -55.3573333333333Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#1
Root: 72.421
--> Root in between the borders! Added to results.
Coefficients: -17.574Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#10
Root: 28.533
--> Root in between the borders! Added to results.
Coefficients: -22.9425714285714Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#2
Root: 35.766
--> Root in between the borders! Added to results.
Coefficients: -42.849Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#3
Root: 59.36
--> Root in between the borders! Added to results.
Coefficients: -4.604Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#4
Root: 8.481
--> Root in between the borders! Added to results.
Coefficients: -11.389Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#5
Root: 19.519
--> Root in between the borders! Added to results.
Coefficients: -25.784Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#6
Root: 39.413
--> Root in between the borders! Added to results.
Coefficients: -30.746Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#7
Root: 45.53
--> Root in between the borders! Added to results.
Coefficients: -66.912Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#8
Root: 83.654
--> Root in between the borders! Added to results.
Coefficients: 3.176Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -6.535
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 80.5486410672961
[20250404_044412.]: Samplename: Sample#1
Root: 80.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.810468482135
[20250404_044412.]: Samplename: Sample#10
Root: 27.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.2641649294309
[20250404_044412.]: Samplename: Sample#2
Root: 46.264
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.1903653427228
[20250404_044412.]: Samplename: Sample#3
Root: 57.19
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.63886339746086
[20250404_044413.]: Samplename: Sample#4
Root: 8.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2162393845509
[20250404_044413.]: Samplename: Sample#5
Root: 24.216
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.6394430638471
[20250404_044413.]: Samplename: Sample#6
Root: 39.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.3080887012493
[20250404_044413.]: Samplename: Sample#7
Root: 44.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.3259098830063
[20250404_044413.]: Samplename: Sample#8
Root: 87.326
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.17959639730045
[20250404_044413.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.18
--> '-10 < root < 0' --> substitute 0
[20250404_044413.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 76.7568961192102
[20250404_044413.]: Samplename: Sample#1
Root: 76.757
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.8326630603664
[20250404_044413.]: Samplename: Sample#10
Root: 28.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.0145327025204
[20250404_044413.]: Samplename: Sample#2
Root: 43.015
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.6144798147902
[20250404_044413.]: Samplename: Sample#3
Root: 57.614
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.86517972238162
[20250404_044413.]: Samplename: Sample#4
Root: 8.865
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.1849817550475
[20250404_044413.]: Samplename: Sample#5
Root: 22.185
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.1973843238972
[20250404_044413.]: Samplename: Sample#6
Root: 29.197
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.9174258632975
[20250404_044413.]: Samplename: Sample#7
Root: 43.917
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6607695784409
[20250404_044413.]: Samplename: Sample#8
Root: 85.661
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.551158207550385
[20250404_044413.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.551
--> '-10 < root < 0' --> substitute 0
[20250404_044413.]: Entered 'solving_equations'-Function
[20250404_044413.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_044413.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_044413.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_044413.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_044413.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_044413.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_044413.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_044413.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_044413.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_044413.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_044413.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_044413.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_044413.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_044413.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_044413.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_044413.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_044413.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_044413.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_044413.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_044413.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_044413.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_044413.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_044413.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_044413.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_044413.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_044413.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_044413.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_044413.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_044413.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_044413.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_044413.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_044413.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_044413.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_044413.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_044413.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_044413.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_044413.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_044413.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_044413.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_044413.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_044413.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_044413.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_044413.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_044413.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_044413.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_044413.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_044413.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_044413.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_044413.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_044413.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_044413.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_044413.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[20250404_044517.]: Entered 'clean_dt'-Function
[20250404_044517.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044517.]: got experimental data
[20250404_044517.]: Entered 'clean_dt'-Function
[20250404_044517.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data)
[20250404_044517.]: got experimental data
[20250404_044518.]: Entered 'clean_dt'-Function
[20250404_044518.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044518.]: got calibration data
[20250404_044518.]: Entered 'clean_dt'-Function
[20250404_044518.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044518.]: got calibration data
[20250404_044518.]: Entered 'hyperbolic_regression'-Function
[20250404_044518.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• On CRAN (4): 'test-algorithm_minmax_FALSE.R:80:5',
'test-algorithm_minmax_TRUE.R:76:5', 'test-hyperbolic.R:27:5',
'test-lints.R:12:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-algorithm_minmax_FALSE_re.R:170:5'): algorithm test, type 1, minmax = FALSE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_FALSE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-algorithm_minmax_TRUE_re.R:170:5'): algorithm test, type 1, minmax = TRUE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_TRUE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:17:5'): test normal function of file import of type 1 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:17:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:65:5'): test normal function of file import of type 2 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:65:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-create_aggregated.R:19:5'): test functioning of aggregated function ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-create_aggregated.R:19:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
Error: Test failures
Execution halted
Error in deferred_run(env) : could not find function "deferred_run"
Calls: <Anonymous>
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.3.4
Check: tests
Result: ERROR
Running ‘testthat.R’ [105s/125s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rBiasCorrection)
>
> local_edition(3)
>
> test_check("rBiasCorrection")
[20250404_172317.]: Entered 'clean_dt'-Function
[20250404_172317.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172317.]: got experimental data
[20250404_172317.]: Entered 'clean_dt'-Function
[20250404_172317.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172317.]: got calibration data
[20250404_172317.]: ### Starting with regression calculations ###
[20250404_172317.]: Entered 'regression_type1'-Function
[20250404_172318.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172318.]: Logging df_agg: CpG#1
[20250404_172318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172318.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_172318.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172318.]: Entered 'hyperbolic_regression'-Function
[20250404_172318.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172318.]: Entered 'cubic_regression'-Function
[20250404_172318.]: 'cubic_regression': minmax = FALSE
[20250404_172318.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172318.]: Logging df_agg: CpG#2
[20250404_172318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172318.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_172318.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172318.]: Entered 'hyperbolic_regression'-Function
[20250404_172318.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172318.]: Entered 'cubic_regression'-Function
[20250404_172318.]: 'cubic_regression': minmax = FALSE
[20250404_172318.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172318.]: Logging df_agg: CpG#3
[20250404_172318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172318.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_172318.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172318.]: Entered 'hyperbolic_regression'-Function
[20250404_172318.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172318.]: Entered 'cubic_regression'-Function
[20250404_172318.]: 'cubic_regression': minmax = FALSE
[20250404_172318.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172318.]: Logging df_agg: CpG#4
[20250404_172318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172318.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_172318.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172318.]: Entered 'hyperbolic_regression'-Function
[20250404_172318.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172319.]: Entered 'cubic_regression'-Function
[20250404_172319.]: 'cubic_regression': minmax = FALSE
[20250404_172319.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172319.]: Logging df_agg: CpG#5
[20250404_172319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172319.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_172319.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172319.]: Entered 'hyperbolic_regression'-Function
[20250404_172319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172319.]: Entered 'cubic_regression'-Function
[20250404_172319.]: 'cubic_regression': minmax = FALSE
[20250404_172318.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172318.]: Logging df_agg: CpG#6
[20250404_172318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172318.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_172318.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172318.]: Entered 'hyperbolic_regression'-Function
[20250404_172318.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172319.]: Entered 'cubic_regression'-Function
[20250404_172319.]: 'cubic_regression': minmax = FALSE
[20250404_172319.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172319.]: Logging df_agg: CpG#7
[20250404_172319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172319.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_172319.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172319.]: Entered 'hyperbolic_regression'-Function
[20250404_172319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172319.]: Entered 'cubic_regression'-Function
[20250404_172319.]: 'cubic_regression': minmax = FALSE
[20250404_172319.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172319.]: Logging df_agg: CpG#8
[20250404_172319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172319.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_172319.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172319.]: Entered 'hyperbolic_regression'-Function
[20250404_172319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172319.]: Entered 'cubic_regression'-Function
[20250404_172319.]: 'cubic_regression': minmax = FALSE
[20250404_172319.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172319.]: Logging df_agg: CpG#9
[20250404_172319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172319.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_172319.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172319.]: Entered 'hyperbolic_regression'-Function
[20250404_172319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172319.]: Entered 'cubic_regression'-Function
[20250404_172319.]: 'cubic_regression': minmax = FALSE
[20250404_172319.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172319.]: Logging df_agg: row_means
[20250404_172319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172319.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_172319.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172319.]: Entered 'hyperbolic_regression'-Function
[20250404_172319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172319.]: Entered 'cubic_regression'-Function
[20250404_172319.]: 'cubic_regression': minmax = FALSE
[20250404_172322.]: Entered 'regression_type1'-Function
[20250404_172322.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172322.]: Logging df_agg: CpG#1
[20250404_172322.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172322.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_172322.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172322.]: Entered 'hyperbolic_regression'-Function
[20250404_172322.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172323.]: Entered 'cubic_regression'-Function
[20250404_172323.]: 'cubic_regression': minmax = FALSE
[20250404_172323.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172323.]: Logging df_agg: CpG#2
[20250404_172323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172323.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_172323.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172323.]: Entered 'hyperbolic_regression'-Function
[20250404_172323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172323.]: Entered 'cubic_regression'-Function
[20250404_172323.]: 'cubic_regression': minmax = FALSE
[20250404_172323.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172323.]: Logging df_agg: CpG#3
[20250404_172323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172323.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_172323.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172323.]: Entered 'hyperbolic_regression'-Function
[20250404_172323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172323.]: Entered 'cubic_regression'-Function
[20250404_172323.]: 'cubic_regression': minmax = FALSE
[20250404_172323.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172323.]: Logging df_agg: CpG#4
[20250404_172323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172323.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_172323.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172323.]: Entered 'hyperbolic_regression'-Function
[20250404_172323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172323.]: Entered 'cubic_regression'-Function
[20250404_172323.]: 'cubic_regression': minmax = FALSE
[20250404_172323.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172323.]: Logging df_agg: CpG#5
[20250404_172323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172323.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_172323.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172323.]: Entered 'hyperbolic_regression'-Function
[20250404_172323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172323.]: Entered 'cubic_regression'-Function
[20250404_172323.]: 'cubic_regression': minmax = FALSE
[20250404_172322.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172323.]: Logging df_agg: CpG#6
[20250404_172323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172323.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_172323.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172323.]: Entered 'hyperbolic_regression'-Function
[20250404_172323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172323.]: Entered 'cubic_regression'-Function
[20250404_172323.]: 'cubic_regression': minmax = FALSE
[20250404_172323.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172323.]: Logging df_agg: CpG#7
[20250404_172323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172323.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_172323.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172323.]: Entered 'hyperbolic_regression'-Function
[20250404_172323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172323.]: Entered 'cubic_regression'-Function
[20250404_172323.]: 'cubic_regression': minmax = FALSE
[20250404_172323.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172323.]: Logging df_agg: CpG#8
[20250404_172323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172323.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_172323.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172323.]: Entered 'hyperbolic_regression'-Function
[20250404_172323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172323.]: Entered 'cubic_regression'-Function
[20250404_172323.]: 'cubic_regression': minmax = FALSE
[20250404_172323.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172323.]: Logging df_agg: CpG#9
[20250404_172323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172323.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_172323.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172323.]: Entered 'hyperbolic_regression'-Function
[20250404_172323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172324.]: Entered 'cubic_regression'-Function
[20250404_172324.]: 'cubic_regression': minmax = FALSE
[20250404_172324.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172324.]: Logging df_agg: row_means
[20250404_172324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172324.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_172324.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172324.]: Entered 'hyperbolic_regression'-Function
[20250404_172324.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172324.]: Entered 'cubic_regression'-Function
[20250404_172324.]: 'cubic_regression': minmax = FALSE
[20250404_172325.]: Entered 'clean_dt'-Function
[20250404_172325.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172325.]: got experimental data
[20250404_172325.]: Entered 'clean_dt'-Function
[20250404_172325.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172325.]: got calibration data
[20250404_172325.]: ### Starting with regression calculations ###
[20250404_172325.]: Entered 'regression_type1'-Function
[20250404_172326.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172326.]: Logging df_agg: CpG#1
[20250404_172326.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172326.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_172326.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172326.]: Entered 'hyperbolic_regression'-Function
[20250404_172326.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172326.]: Entered 'cubic_regression'-Function
[20250404_172326.]: 'cubic_regression': minmax = FALSE
[20250404_172326.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172326.]: Logging df_agg: CpG#2
[20250404_172326.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172326.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_172326.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172326.]: Entered 'hyperbolic_regression'-Function
[20250404_172326.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172327.]: Entered 'cubic_regression'-Function
[20250404_172327.]: 'cubic_regression': minmax = FALSE
[20250404_172327.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172327.]: Logging df_agg: CpG#3
[20250404_172327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172327.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_172327.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172327.]: Entered 'hyperbolic_regression'-Function
[20250404_172327.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172327.]: Entered 'cubic_regression'-Function
[20250404_172327.]: 'cubic_regression': minmax = FALSE
[20250404_172327.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172327.]: Logging df_agg: CpG#4
[20250404_172327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172327.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_172327.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172327.]: Entered 'hyperbolic_regression'-Function
[20250404_172327.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172327.]: Entered 'cubic_regression'-Function
[20250404_172327.]: 'cubic_regression': minmax = FALSE
[20250404_172327.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172327.]: Logging df_agg: CpG#5
[20250404_172327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172327.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_172327.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172327.]: Entered 'hyperbolic_regression'-Function
[20250404_172327.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172327.]: Entered 'cubic_regression'-Function
[20250404_172327.]: 'cubic_regression': minmax = FALSE
[20250404_172326.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172326.]: Logging df_agg: CpG#6
[20250404_172326.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172326.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_172326.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172326.]: Entered 'hyperbolic_regression'-Function
[20250404_172326.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172327.]: Entered 'cubic_regression'-Function
[20250404_172327.]: 'cubic_regression': minmax = FALSE
[20250404_172327.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172327.]: Logging df_agg: CpG#7
[20250404_172327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172327.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_172327.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172327.]: Entered 'hyperbolic_regression'-Function
[20250404_172327.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172327.]: Entered 'cubic_regression'-Function
[20250404_172327.]: 'cubic_regression': minmax = FALSE
[20250404_172327.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172327.]: Logging df_agg: CpG#8
[20250404_172327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172327.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_172327.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172327.]: Entered 'hyperbolic_regression'-Function
[20250404_172327.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172327.]: Entered 'cubic_regression'-Function
[20250404_172327.]: 'cubic_regression': minmax = FALSE
[20250404_172327.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172327.]: Logging df_agg: CpG#9
[20250404_172327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172327.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_172327.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172327.]: Entered 'hyperbolic_regression'-Function
[20250404_172327.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172327.]: Entered 'cubic_regression'-Function
[20250404_172327.]: 'cubic_regression': minmax = FALSE
[20250404_172327.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172327.]: Logging df_agg: row_means
[20250404_172327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172327.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_172327.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172327.]: Entered 'hyperbolic_regression'-Function
[20250404_172327.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172328.]: Entered 'cubic_regression'-Function
[20250404_172328.]: 'cubic_regression': minmax = FALSE
[20250404_172330.]: Entered 'regression_type1'-Function
[20250404_172331.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172331.]: Logging df_agg: CpG#1
[20250404_172331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172331.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_172331.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172331.]: Entered 'hyperbolic_regression'-Function
[20250404_172331.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172331.]: Entered 'cubic_regression'-Function
[20250404_172331.]: 'cubic_regression': minmax = FALSE
[20250404_172331.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172331.]: Logging df_agg: CpG#2
[20250404_172331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172331.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_172331.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172331.]: Entered 'hyperbolic_regression'-Function
[20250404_172331.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172332.]: Entered 'cubic_regression'-Function
[20250404_172332.]: 'cubic_regression': minmax = FALSE
[20250404_172332.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172332.]: Logging df_agg: CpG#3
[20250404_172332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172332.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_172332.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172332.]: Entered 'hyperbolic_regression'-Function
[20250404_172332.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172332.]: Entered 'cubic_regression'-Function
[20250404_172332.]: 'cubic_regression': minmax = FALSE
[20250404_172332.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172332.]: Logging df_agg: CpG#4
[20250404_172332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172332.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_172332.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172332.]: Entered 'hyperbolic_regression'-Function
[20250404_172332.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172333.]: Entered 'cubic_regression'-Function
[20250404_172333.]: 'cubic_regression': minmax = FALSE
[20250404_172333.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172333.]: Logging df_agg: CpG#5
[20250404_172333.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172333.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_172333.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172333.]: Entered 'hyperbolic_regression'-Function
[20250404_172333.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172333.]: Entered 'cubic_regression'-Function
[20250404_172333.]: 'cubic_regression': minmax = FALSE
[20250404_172331.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172331.]: Logging df_agg: CpG#6
[20250404_172331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172331.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_172331.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172331.]: Entered 'hyperbolic_regression'-Function
[20250404_172331.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172332.]: Entered 'cubic_regression'-Function
[20250404_172332.]: 'cubic_regression': minmax = FALSE
[20250404_172332.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172332.]: Logging df_agg: CpG#7
[20250404_172332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172332.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_172332.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172332.]: Entered 'hyperbolic_regression'-Function
[20250404_172332.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172332.]: Entered 'cubic_regression'-Function
[20250404_172332.]: 'cubic_regression': minmax = FALSE
[20250404_172332.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172332.]: Logging df_agg: CpG#8
[20250404_172332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172332.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_172332.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172332.]: Entered 'hyperbolic_regression'-Function
[20250404_172332.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172332.]: Entered 'cubic_regression'-Function
[20250404_172332.]: 'cubic_regression': minmax = FALSE
[20250404_172332.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172332.]: Logging df_agg: CpG#9
[20250404_172332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172332.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_172332.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172332.]: Entered 'hyperbolic_regression'-Function
[20250404_172332.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172332.]: Entered 'cubic_regression'-Function
[20250404_172332.]: 'cubic_regression': minmax = FALSE
[20250404_172332.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172332.]: Logging df_agg: row_means
[20250404_172332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172332.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_172332.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172332.]: Entered 'hyperbolic_regression'-Function
[20250404_172332.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172333.]: Entered 'cubic_regression'-Function
[20250404_172333.]: 'cubic_regression': minmax = FALSE
[20250404_172334.]: Entered 'solving_equations'-Function
[20250404_172334.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_172334.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_172334.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_172334.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_172334.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_172334.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_172334.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_172334.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_172334.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 1.13660501904968
[20250404_172334.]: Samplename: 0
Root: 1.137
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.4129696733689
[20250404_172334.]: Samplename: 12.5
Root: 11.413
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.174000526428
[20250404_172334.]: Samplename: 25
Root: 26.174
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.1050449117028
[20250404_172334.]: Samplename: 37.5
Root: 35.105
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.685500330611
[20250404_172334.]: Samplename: 50
Root: 47.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.1440494417104
[20250404_172334.]: Samplename: 62.5
Root: 67.144
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7644668894086
[20250404_172334.]: Samplename: 75
Root: 75.764
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.4054158616395
[20250404_172334.]: Samplename: 87.5
Root: 84.405
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.94827248399
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.948
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_172334.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_172334.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_172334.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_172334.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_172334.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_172334.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_172334.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_172334.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_172334.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_172334.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_172334.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_172334.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_172334.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_172334.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_172334.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_172334.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 2.41558626275183
[20250404_172334.]: Samplename: 0
Root: 2.416
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.1649674907454
[20250404_172334.]: Samplename: 12.5
Root: 10.165
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.9830820412762
[20250404_172334.]: Samplename: 25
Root: 23.983
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2773619900429
[20250404_172334.]: Samplename: 37.5
Root: 37.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.8659386543864
[20250404_172334.]: Samplename: 50
Root: 50.866
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.4342273571069
[20250404_172334.]: Samplename: 62.5
Root: 62.434
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.3915260534323
[20250404_172334.]: Samplename: 75
Root: 76.392
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.159788778566
[20250404_172334.]: Samplename: 87.5
Root: 86.16
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.267759893323
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.268
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_172334.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_172334.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_172334.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_172334.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_172334.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_172334.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_172334.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_172334.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_172334.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_172334.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_172334.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_172334.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_172334.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_172334.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_172334.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_172334.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 2.80068218205093
[20250404_172334.]: Samplename: 0
Root: 2.801
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.27535134596596
[20250404_172334.]: Samplename: 12.5
Root: 9.275
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4762621928197
[20250404_172334.]: Samplename: 25
Root: 25.476
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0122075735416
[20250404_172334.]: Samplename: 37.5
Root: 34.012
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.7842655662325
[20250404_172334.]: Samplename: 50
Root: 51.784
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.6732311906145
[20250404_172334.]: Samplename: 62.5
Root: 64.673
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.4326978859189
[20250404_172334.]: Samplename: 75
Root: 78.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.3427232852719
[20250404_172334.]: Samplename: 87.5
Root: 81.343
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.964406640583
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.964
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: -2.13403721845678
[20250404_172334.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.134
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.5082192457956
[20250404_172334.]: Samplename: 12.5
Root: 10.508
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9164567253388
[20250404_172334.]: Samplename: 25
Root: 26.916
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.8334779159501
[20250404_172334.]: Samplename: 37.5
Root: 36.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.0097895977263
[20250404_172334.]: Samplename: 50
Root: 52.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8930527921581
[20250404_172334.]: Samplename: 62.5
Root: 64.893
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.5671055499357
[20250404_172334.]: Samplename: 75
Root: 74.567
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.5294954832669
[20250404_172334.]: Samplename: 87.5
Root: 84.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.047146466811
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.047
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_172334.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_172334.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_172334.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_172334.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_172334.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_172334.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_172334.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_172334.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_172334.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_172334.]: ### Starting with regression calculations ###
[20250404_172334.]: Entered 'regression_type1'-Function
[20250404_172335.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_172335.]: Logging df_agg: CpG#1
[20250404_172335.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172335.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_172335.]: Entered 'hyperbolic_regression'-Function
[20250404_172335.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172336.]: Entered 'cubic_regression'-Function
[20250404_172336.]: 'cubic_regression': minmax = FALSE
[20250404_172336.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_172336.]: Logging df_agg: CpG#2
[20250404_172336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172336.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_172336.]: Entered 'hyperbolic_regression'-Function
[20250404_172336.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172336.]: Entered 'cubic_regression'-Function
[20250404_172336.]: 'cubic_regression': minmax = FALSE
[20250404_172336.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_172336.]: Logging df_agg: CpG#3
[20250404_172336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172336.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_172336.]: Entered 'hyperbolic_regression'-Function
[20250404_172336.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172336.]: Entered 'cubic_regression'-Function
[20250404_172336.]: 'cubic_regression': minmax = FALSE
[20250404_172336.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_172336.]: Logging df_agg: CpG#4
[20250404_172336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172336.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_172336.]: Entered 'hyperbolic_regression'-Function
[20250404_172336.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172336.]: Entered 'cubic_regression'-Function
[20250404_172336.]: 'cubic_regression': minmax = FALSE
[20250404_172336.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_172336.]: Logging df_agg: CpG#5
[20250404_172336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172336.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_172336.]: Entered 'hyperbolic_regression'-Function
[20250404_172336.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172336.]: Entered 'cubic_regression'-Function
[20250404_172336.]: 'cubic_regression': minmax = FALSE
[20250404_172336.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_172336.]: Logging df_agg: CpG#6
[20250404_172336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172336.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_172336.]: Entered 'hyperbolic_regression'-Function
[20250404_172336.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172336.]: Entered 'cubic_regression'-Function
[20250404_172336.]: 'cubic_regression': minmax = FALSE
[20250404_172336.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_172336.]: Logging df_agg: CpG#7
[20250404_172336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172336.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_172336.]: Entered 'hyperbolic_regression'-Function
[20250404_172336.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172336.]: Entered 'cubic_regression'-Function
[20250404_172336.]: 'cubic_regression': minmax = FALSE
[20250404_172336.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_172336.]: Logging df_agg: CpG#8
[20250404_172336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172336.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_172336.]: Entered 'hyperbolic_regression'-Function
[20250404_172336.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172336.]: Entered 'cubic_regression'-Function
[20250404_172336.]: 'cubic_regression': minmax = FALSE
[20250404_172336.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_172336.]: Logging df_agg: CpG#9
[20250404_172336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172336.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_172336.]: Entered 'hyperbolic_regression'-Function
[20250404_172336.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172337.]: Entered 'cubic_regression'-Function
[20250404_172337.]: 'cubic_regression': minmax = FALSE
[20250404_172337.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_172337.]: Logging df_agg: row_means
[20250404_172337.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172337.]: c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_172337.]: Entered 'hyperbolic_regression'-Function
[20250404_172337.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172337.]: Entered 'cubic_regression'-Function
[20250404_172337.]: 'cubic_regression': minmax = FALSE
[20250404_172338.]: Entered 'solving_equations'-Function
[20250404_172338.]: Solving cubic regression for CpG#1
Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_172338.]: Samplename: 0
Root: 1.334
--> Root in between the borders! Added to results.
Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_172338.]: Samplename: 12.5
Root: 11.446
--> Root in between the borders! Added to results.
Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_172338.]: Samplename: 25
Root: 22.228
--> Root in between the borders! Added to results.
Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_172338.]: Samplename: 37.5
Root: 36.374
--> Root in between the borders! Added to results.
Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_172338.]: Samplename: 50
Root: 52.044
--> Root in between the borders! Added to results.
Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_172338.]: Samplename: 62.5
Root: 66.144
--> Root in between the borders! Added to results.
Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_172338.]: Samplename: 75
Root: 75.864
--> Root in between the borders! Added to results.
Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_172338.]: Samplename: 87.5
Root: 82.254
--> Root in between the borders! Added to results.
Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.877
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172338.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172338.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172338.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172338.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172338.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172338.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172338.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172338.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: Solving cubic regression for CpG#3
Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_172338.]: Samplename: 0
Root: 1.441
--> Root in between the borders! Added to results.
Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_172338.]: Samplename: 12.5
Root: 10.568
--> Root in between the borders! Added to results.
Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_172338.]: Samplename: 25
Root: 24.796
--> Root in between the borders! Added to results.
Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_172338.]: Samplename: 37.5
Root: 35.952
--> Root in between the borders! Added to results.
Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_172338.]: Samplename: 50
Root: 50.684
--> Root in between the borders! Added to results.
Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_172338.]: Samplename: 62.5
Root: 65.142
--> Root in between the borders! Added to results.
Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_172338.]: Samplename: 75
Root: 77.905
--> Root in between the borders! Added to results.
Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_172338.]: Samplename: 87.5
Root: 80.767
--> Root in between the borders! Added to results.
Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.38
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: Solving cubic regression for CpG#4
Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_172338.]: Samplename: 0
Root: 0.858
--> Root in between the borders! Added to results.
Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_172338.]: Samplename: 12.5
Root: 12.086
--> Root in between the borders! Added to results.
Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_172338.]: Samplename: 25
Root: 23.316
--> Root in between the borders! Added to results.
Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_172338.]: Samplename: 37.5
Root: 37.383
--> Root in between the borders! Added to results.
Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_172338.]: Samplename: 50
Root: 48.353
--> Root in between the borders! Added to results.
Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_172338.]: Samplename: 62.5
Root: 68.082
--> Root in between the borders! Added to results.
Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_172338.]: Samplename: 75
Root: 74.615
--> Root in between the borders! Added to results.
Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_172338.]: Samplename: 87.5
Root: 83.254
--> Root in between the borders! Added to results.
Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.715
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172338.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172338.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172338.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172338.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172338.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172338.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172338.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172338.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: Solving cubic regression for CpG#6
Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_172338.]: Samplename: 0
Root: 0.349
--> Root in between the borders! Added to results.
Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_172338.]: Samplename: 12.5
Root: 11.718
--> Root in between the borders! Added to results.
Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_172338.]: Samplename: 25
Root: 26.396
--> Root in between the borders! Added to results.
Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_172338.]: Samplename: 37.5
Root: 35.301
--> Root in between the borders! Added to results.
Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_172338.]: Samplename: 50
Root: 50.134
--> Root in between the borders! Added to results.
Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_172338.]: Samplename: 62.5
Root: 64.993
--> Root in between the borders! Added to results.
Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_172338.]: Samplename: 75
Root: 73.639
--> Root in between the borders! Added to results.
Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_172338.]: Samplename: 87.5
Root: 87.043
--> Root in between the borders! Added to results.
Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.329
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: Solving cubic regression for CpG#7
Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_172338.]: Samplename: 0
Root: 2.13
--> Root in between the borders! Added to results.
Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_172338.]: Samplename: 12.5
Root: 9.973
--> Root in between the borders! Added to results.
Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_172338.]: Samplename: 25
Root: 22.206
--> Root in between the borders! Added to results.
Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_172338.]: Samplename: 37.5
Root: 35.814
--> Root in between the borders! Added to results.
Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_172338.]: Samplename: 50
Root: 53.28
--> Root in between the borders! Added to results.
Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_172338.]: Samplename: 62.5
Root: 66.598
--> Root in between the borders! Added to results.
Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_172338.]: Samplename: 75
Root: 76.575
--> Root in between the borders! Added to results.
Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_172338.]: Samplename: 87.5
Root: 80.219
--> Root in between the borders! Added to results.
Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.506
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172338.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172338.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172338.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172338.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172338.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172338.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172338.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172338.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172338.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172338.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172338.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172338.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172338.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172338.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172338.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172338.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: Solving cubic regression for row_means
Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_172338.]: Samplename: 0
Root: 1.287
--> Root in between the borders! Added to results.
Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_172338.]: Samplename: 12.5
Root: 10.847
--> Root in between the borders! Added to results.
Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_172338.]: Samplename: 25
Root: 24.737
--> Root in between the borders! Added to results.
Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_172338.]: Samplename: 37.5
Root: 36.02
--> Root in between the borders! Added to results.
Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_172338.]: Samplename: 50
Root: 50.639
--> Root in between the borders! Added to results.
Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_172338.]: Samplename: 62.5
Root: 65.497
--> Root in between the borders! Added to results.
Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_172338.]: Samplename: 75
Root: 75.751
--> Root in between the borders! Added to results.
Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_172338.]: Samplename: 87.5
Root: 83.403
--> Root in between the borders! Added to results.
Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_172338.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.573
--> '100 < root < 110' --> substitute 100
[20250404_172338.]: ### Starting with regression calculations ###
[20250404_172338.]: Entered 'regression_type1'-Function
[20250404_172339.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_172339.]: Logging df_agg: CpG#1
[20250404_172339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172339.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_172339.]: Entered 'hyperbolic_regression'-Function
[20250404_172339.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172340.]: Entered 'cubic_regression'-Function
[20250404_172340.]: 'cubic_regression': minmax = FALSE
[20250404_172340.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_172340.]: Logging df_agg: CpG#2
[20250404_172340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172340.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_172340.]: Entered 'hyperbolic_regression'-Function
[20250404_172340.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172340.]: Entered 'cubic_regression'-Function
[20250404_172340.]: 'cubic_regression': minmax = FALSE
[20250404_172340.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_172340.]: Logging df_agg: CpG#3
[20250404_172340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172340.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_172340.]: Entered 'hyperbolic_regression'-Function
[20250404_172340.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172340.]: Entered 'cubic_regression'-Function
[20250404_172340.]: 'cubic_regression': minmax = FALSE
[20250404_172340.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_172340.]: Logging df_agg: CpG#4
[20250404_172340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172340.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_172340.]: Entered 'hyperbolic_regression'-Function
[20250404_172340.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172340.]: Entered 'cubic_regression'-Function
[20250404_172340.]: 'cubic_regression': minmax = FALSE
[20250404_172340.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_172340.]: Logging df_agg: CpG#5
[20250404_172340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172340.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_172340.]: Entered 'hyperbolic_regression'-Function
[20250404_172340.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172341.]: Entered 'cubic_regression'-Function
[20250404_172341.]: 'cubic_regression': minmax = FALSE
[20250404_172339.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_172340.]: Logging df_agg: CpG#6
[20250404_172340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172340.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_172340.]: Entered 'hyperbolic_regression'-Function
[20250404_172340.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172340.]: Entered 'cubic_regression'-Function
[20250404_172340.]: 'cubic_regression': minmax = FALSE
[20250404_172340.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_172340.]: Logging df_agg: CpG#7
[20250404_172340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172340.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_172340.]: Entered 'hyperbolic_regression'-Function
[20250404_172340.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172340.]: Entered 'cubic_regression'-Function
[20250404_172340.]: 'cubic_regression': minmax = FALSE
[20250404_172340.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_172340.]: Logging df_agg: CpG#8
[20250404_172340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172340.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_172340.]: Entered 'hyperbolic_regression'-Function
[20250404_172340.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172340.]: Entered 'cubic_regression'-Function
[20250404_172340.]: 'cubic_regression': minmax = FALSE
[20250404_172340.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_172340.]: Logging df_agg: CpG#9
[20250404_172340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172340.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_172340.]: Entered 'hyperbolic_regression'-Function
[20250404_172340.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172340.]: Entered 'cubic_regression'-Function
[20250404_172340.]: 'cubic_regression': minmax = FALSE
[20250404_172340.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_172340.]: Logging df_agg: row_means
[20250404_172340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172340.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_172340.]: Entered 'hyperbolic_regression'-Function
[20250404_172340.]: 'hyperbolic_regression': minmax = FALSE
[20250404_172341.]: Entered 'cubic_regression'-Function
[20250404_172341.]: 'cubic_regression': minmax = FALSE
[20250404_172342.]: Entered 'solving_equations'-Function
[20250404_172342.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 79.8673456895745
[20250404_172342.]: Samplename: Sample#1
Root: 79.867
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.7900184340805
[20250404_172342.]: Samplename: Sample#10
Root: 29.79
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.6525415639691
[20250404_172342.]: Samplename: Sample#2
Root: 41.653
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.4652090254513
[20250404_172342.]: Samplename: Sample#3
Root: 57.465
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.2007130627765
[20250404_172342.]: Samplename: Sample#4
Root: 9.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8059600538131
[20250404_172342.]: Samplename: Sample#5
Root: 21.806
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.083796735881
[20250404_172342.]: Samplename: Sample#6
Root: 23.084
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.5034245569385
[20250404_172342.]: Samplename: Sample#7
Root: 45.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6987904075704
[20250404_172342.]: Samplename: Sample#8
Root: 85.699
--> Root in between the borders! Added to results.
Hyperbolic solved: -3.66512807265101
[20250404_172342.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -3.665
--> '-10 < root < 0' --> substitute 0
[20250404_172342.]: Solving cubic regression for CpG#2
Coefficients: -60.0166632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#1
Root: 76.388
--> Root in between the borders! Added to results.
Coefficients: -19.33132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#10
Root: 31.437
--> Root in between the borders! Added to results.
Coefficients: -28.1616632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#2
Root: 42.956
--> Root in between the borders! Added to results.
Coefficients: -42.07832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#3
Root: 58.838
--> Root in between the borders! Added to results.
Coefficients: -2.49332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#4
Root: 4.715
--> Root in between the borders! Added to results.
Coefficients: -11.94832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#5
Root: 20.644
--> Root in between the borders! Added to results.
Coefficients: -10.36332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#6
Root: 18.159
--> Root in between the borders! Added to results.
Coefficients: -26.77132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#7
Root: 41.228
--> Root in between the borders! Added to results.
Coefficients: -70.81532996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#8
Root: 85.785
--> Root in between the borders! Added to results.
Coefficients: -1.41332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: Sample#9
Root: 2.703
--> Root in between the borders! Added to results.
[20250404_172342.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.9349254100163
[20250404_172342.]: Samplename: Sample#1
Root: 74.935
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.6844381581493
[20250404_172342.]: Samplename: Sample#10
Root: 27.684
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.852019114379
[20250404_172342.]: Samplename: Sample#2
Root: 41.852
--> Root in between the borders! Added to results.
Hyperbolic solved: 55.8325180209418
[20250404_172342.]: Samplename: Sample#3
Root: 55.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.03519251633153
[20250404_172342.]: Samplename: Sample#4
Root: 8.035
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.1066315721853
[20250404_172342.]: Samplename: Sample#5
Root: 24.107
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2419820027673
[20250404_172342.]: Samplename: Sample#6
Root: 26.242
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0944922703422
[20250404_172342.]: Samplename: Sample#7
Root: 44.094
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.8279382585787
[20250404_172342.]: Samplename: Sample#8
Root: 85.828
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.666482392725758
[20250404_172342.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.666
--> '-10 < root < 0' --> substitute 0
[20250404_172342.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 76.3495278640236
[20250404_172342.]: Samplename: Sample#1
Root: 76.35
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.2568553570941
[20250404_172342.]: Samplename: Sample#10
Root: 28.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4089839390807
[20250404_172342.]: Samplename: Sample#2
Root: 43.409
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.5435236860146
[20250404_172342.]: Samplename: Sample#3
Root: 58.544
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.3087045690571
[20250404_172342.]: Samplename: Sample#4
Root: 10.309
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.183045165659
[20250404_172342.]: Samplename: Sample#5
Root: 22.183
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.1337769553499
[20250404_172342.]: Samplename: Sample#6
Root: 27.134
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8321096080155
[20250404_172342.]: Samplename: Sample#7
Root: 41.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6890189074743
[20250404_172342.]: Samplename: Sample#8
Root: 85.689
--> Root in between the borders! Added to results.
Hyperbolic solved: 2.42232098177269
[20250404_172342.]: Samplename: Sample#9
Root: 2.422
--> Root in between the borders! Added to results.
[20250404_172342.]: Solving cubic regression for CpG#5
Coefficients: -48.4612946127946Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -14.2119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#10
Root: 27.256
--> Root in between the borders! Added to results.
Coefficients: -25.9451041366041Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#2
Root: 44.648
--> Root in between the borders! Added to results.
Coefficients: -32.6879612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#3
Root: 53.538
--> Root in between the borders! Added to results.
Coefficients: -4.69796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#4
Root: 10.206
--> Root in between the borders! Added to results.
Coefficients: -12.0579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#5
Root: 23.695
--> Root in between the borders! Added to results.
Coefficients: -13.9179612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#6
Root: 26.778
--> Root in between the borders! Added to results.
Coefficients: -24.9119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#7
Root: 43.226
--> Root in between the borders! Added to results.
Coefficients: -63.7579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#8
Root: 88.581
--> Root in between the borders! Added to results.
Coefficients: -0.587961279461277Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172342.]: Samplename: Sample#9
Root: 1.375
--> Root in between the borders! Added to results.
[20250404_172342.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2780593622711
[20250404_172342.]: Samplename: Sample#1
Root: 79.278
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2012458984074
[20250404_172342.]: Samplename: Sample#10
Root: 30.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8474393624107
[20250404_172342.]: Samplename: Sample#2
Root: 41.847
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8423517321508
[20250404_172342.]: Samplename: Sample#3
Root: 56.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.87856046118588
[20250404_172342.]: Samplename: Sample#4
Root: 8.879
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.69015950004
[20250404_172342.]: Samplename: Sample#5
Root: 18.69
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9309263534749
[20250404_172342.]: Samplename: Sample#6
Root: 29.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8148560027697
[20250404_172342.]: Samplename: Sample#7
Root: 42.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.7501831416152
[20250404_172342.]: Samplename: Sample#8
Root: 86.75
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.51516194985267
[20250404_172342.]: Samplename: Sample#9
Root: 1.515
--> Root in between the borders! Added to results.
[20250404_172342.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 78.2565592569279
[20250404_172342.]: Samplename: Sample#1
Root: 78.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.488739349283
[20250404_172342.]: Samplename: Sample#10
Root: 25.489
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.3712258915285
[20250404_172342.]: Samplename: Sample#2
Root: 47.371
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.3142673189298
[20250404_172342.]: Samplename: Sample#3
Root: 58.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7212231360573
[20250404_172342.]: Samplename: Sample#4
Root: 11.721
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.3797485992238
[20250404_172342.]: Samplename: Sample#5
Root: 25.38
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.4095133062523
[20250404_172342.]: Samplename: Sample#6
Root: 29.41
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.5755071469546
[20250404_172342.]: Samplename: Sample#7
Root: 44.576
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.9628731021447
[20250404_172342.]: Samplename: Sample#8
Root: 85.963
--> Root in between the borders! Added to results.
Hyperbolic solved: -4.1645647175353
[20250404_172342.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.165
--> '-10 < root < 0' --> substitute 0
[20250404_172342.]: Solving cubic regression for CpG#8
Coefficients: -56.4535185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#1
Root: 72.337
--> Root in between the borders! Added to results.
Coefficients: -18.6701851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#10
Root: 28.678
--> Root in between the borders! Added to results.
Coefficients: -24.0387566137566Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#2
Root: 35.595
--> Root in between the borders! Added to results.
Coefficients: -43.9451851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#3
Root: 58.861
--> Root in between the borders! Added to results.
Coefficients: -5.70018518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#4
Root: 9.868
--> Root in between the borders! Added to results.
Coefficients: -12.4851851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#5
Root: 20.166
--> Root in between the borders! Added to results.
Coefficients: -26.8801851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#6
Root: 39.117
--> Root in between the borders! Added to results.
Coefficients: -31.8421851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#7
Root: 45.08
--> Root in between the borders! Added to results.
Coefficients: -68.0081851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#8
Root: 84.373
--> Root in between the borders! Added to results.
Coefficients: 2.07981481481482Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172342.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.026
--> '-10 < root < 0' --> substitute 0
[20250404_172342.]: Solving cubic regression for CpG#9
Coefficients: -60.8091986531987Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#1
Root: 81.262
--> Root in between the borders! Added to results.
Coefficients: -14.5538653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#10
Root: 24.569
--> Root in between the borders! Added to results.
Coefficients: -26.6344367484368Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#2
Root: 45.035
--> Root in between the borders! Added to results.
Coefficients: -35.4783653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#3
Root: 57.113
--> Root in between the borders! Added to results.
Coefficients: -4.73586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#4
Root: 7.362
--> Root in between the borders! Added to results.
Coefficients: -12.5308653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#5
Root: 20.907
--> Root in between the borders! Added to results.
Coefficients: -21.9358653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#6
Root: 37.545
--> Root in between the borders! Added to results.
Coefficients: -25.1998653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#7
Root: 42.828
--> Root in between the borders! Added to results.
Coefficients: -70.5118653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#8
Root: 88.082
--> Root in between the borders! Added to results.
Coefficients: -0.505865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172342.]: Samplename: Sample#9
Root: 0.749
--> Root in between the borders! Added to results.
[20250404_172342.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 77.0692797356261
[20250404_172342.]: Samplename: Sample#1
Root: 77.069
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3620040447844
[20250404_172342.]: Samplename: Sample#10
Root: 28.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.5026170660315
[20250404_172342.]: Samplename: Sample#2
Root: 42.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.2972045344154
[20250404_172342.]: Samplename: Sample#3
Root: 57.297
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.82704040274281
[20250404_172342.]: Samplename: Sample#4
Root: 8.827
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8102591233667
[20250404_172342.]: Samplename: Sample#5
Root: 21.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.722865717687
[20250404_172342.]: Samplename: Sample#6
Root: 28.723
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4105098027891
[20250404_172342.]: Samplename: Sample#7
Root: 43.411
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.4143551699061
[20250404_172342.]: Samplename: Sample#8
Root: 86.414
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.237019926848022
[20250404_172342.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.237
--> '-10 < root < 0' --> substitute 0
[20250404_172342.]: Entered 'solving_equations'-Function
[20250404_172342.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_172342.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_172342.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_172342.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_172342.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_172342.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_172342.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_172342.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_172342.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_172342.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_172342.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_172342.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_172342.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_172342.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_172342.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_172342.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_172342.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_172342.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_172342.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_172342.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_172342.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_172342.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_172342.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_172342.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_172342.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_172342.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_172342.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_172342.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_172342.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_172343.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_172343.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_172343.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_172343.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172343.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172343.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172343.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172343.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172343.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172343.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172343.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172343.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_172343.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_172343.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_172343.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_172343.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_172343.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_172343.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_172343.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_172343.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_172343.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_172343.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_172343.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_172343.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_172343.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_172343.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_172343.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_172343.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_172343.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_172343.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_172343.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_172343.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_172343.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_172343.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172343.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172343.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172343.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172343.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172343.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172343.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172343.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172343.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_172343.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_172343.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172343.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172343.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172343.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172343.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172343.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172343.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172343.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172343.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_172343.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_172343.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_172343.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_172343.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_172343.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_172343.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_172343.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_172343.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_172343.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_172343.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_172343.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_172343.]: Entered 'clean_dt'-Function
[20250404_172343.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172343.]: got experimental data
[20250404_172343.]: Entered 'clean_dt'-Function
[20250404_172343.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172343.]: got calibration data
[20250404_172343.]: ### Starting with regression calculations ###
[20250404_172343.]: Entered 'regression_type1'-Function
[20250404_172344.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172344.]: Logging df_agg: CpG#1
[20250404_172344.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172344.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_172344.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172344.]: Entered 'hyperbolic_regression'-Function
[20250404_172344.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172344.]: Entered 'cubic_regression'-Function
[20250404_172344.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172344.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172344.]: Logging df_agg: CpG#2
[20250404_172344.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172344.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_172344.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172344.]: Entered 'hyperbolic_regression'-Function
[20250404_172344.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172344.]: Entered 'cubic_regression'-Function
[20250404_172344.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172344.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172344.]: Logging df_agg: CpG#3
[20250404_172344.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172344.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_172344.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172344.]: Entered 'hyperbolic_regression'-Function
[20250404_172345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: Entered 'cubic_regression'-Function
[20250404_172345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172345.]: Logging df_agg: CpG#4
[20250404_172345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172345.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_172345.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172345.]: Entered 'hyperbolic_regression'-Function
[20250404_172345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: Entered 'cubic_regression'-Function
[20250404_172345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172345.]: Logging df_agg: CpG#5
[20250404_172345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172345.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_172345.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172345.]: Entered 'hyperbolic_regression'-Function
[20250404_172345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: Entered 'cubic_regression'-Function
[20250404_172345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172345.]: Logging df_agg: CpG#6
[20250404_172345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172345.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_172345.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172345.]: Entered 'hyperbolic_regression'-Function
[20250404_172345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: Entered 'cubic_regression'-Function
[20250404_172345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172345.]: Logging df_agg: CpG#7
[20250404_172345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172345.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_172345.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172345.]: Entered 'hyperbolic_regression'-Function
[20250404_172345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: Entered 'cubic_regression'-Function
[20250404_172345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172345.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172345.]: Logging df_agg: CpG#8
[20250404_172345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172345.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_172345.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172345.]: Entered 'hyperbolic_regression'-Function
[20250404_172345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172346.]: Entered 'cubic_regression'-Function
[20250404_172346.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172346.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172346.]: Logging df_agg: CpG#9
[20250404_172346.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172346.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_172346.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172346.]: Entered 'hyperbolic_regression'-Function
[20250404_172346.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172346.]: Entered 'cubic_regression'-Function
[20250404_172346.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172346.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172346.]: Logging df_agg: row_means
[20250404_172346.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172346.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_172346.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172346.]: Entered 'hyperbolic_regression'-Function
[20250404_172346.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172346.]: Entered 'cubic_regression'-Function
[20250404_172346.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172348.]: Entered 'regression_type1'-Function
[20250404_172349.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172349.]: Logging df_agg: CpG#1
[20250404_172349.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172349.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_172349.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172349.]: Entered 'hyperbolic_regression'-Function
[20250404_172349.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: Entered 'cubic_regression'-Function
[20250404_172350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172350.]: Logging df_agg: CpG#2
[20250404_172350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172350.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_172350.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172350.]: Entered 'hyperbolic_regression'-Function
[20250404_172350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: Entered 'cubic_regression'-Function
[20250404_172350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172350.]: Logging df_agg: CpG#3
[20250404_172350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172350.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_172350.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172350.]: Entered 'hyperbolic_regression'-Function
[20250404_172350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: Entered 'cubic_regression'-Function
[20250404_172350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172350.]: Logging df_agg: CpG#4
[20250404_172350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172350.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_172350.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172350.]: Entered 'hyperbolic_regression'-Function
[20250404_172350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: Entered 'cubic_regression'-Function
[20250404_172350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172350.]: Logging df_agg: CpG#5
[20250404_172350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172350.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_172350.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172350.]: Entered 'hyperbolic_regression'-Function
[20250404_172350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172351.]: Entered 'cubic_regression'-Function
[20250404_172351.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172349.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172350.]: Logging df_agg: CpG#6
[20250404_172350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172350.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_172350.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172350.]: Entered 'hyperbolic_regression'-Function
[20250404_172350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: Entered 'cubic_regression'-Function
[20250404_172350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172350.]: Logging df_agg: CpG#7
[20250404_172350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172350.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_172350.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172350.]: Entered 'hyperbolic_regression'-Function
[20250404_172350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: Entered 'cubic_regression'-Function
[20250404_172350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172350.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172350.]: Logging df_agg: CpG#8
[20250404_172350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172350.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_172350.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172350.]: Entered 'hyperbolic_regression'-Function
[20250404_172350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172351.]: Entered 'cubic_regression'-Function
[20250404_172351.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172351.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172351.]: Logging df_agg: CpG#9
[20250404_172351.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172351.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_172351.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172351.]: Entered 'hyperbolic_regression'-Function
[20250404_172351.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172351.]: Entered 'cubic_regression'-Function
[20250404_172351.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172351.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172351.]: Logging df_agg: row_means
[20250404_172351.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172351.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_172351.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172351.]: Entered 'hyperbolic_regression'-Function
[20250404_172351.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172351.]: Entered 'cubic_regression'-Function
[20250404_172351.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172353.]: Entered 'clean_dt'-Function
[20250404_172353.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172353.]: got experimental data
[20250404_172353.]: Entered 'clean_dt'-Function
[20250404_172353.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172353.]: got calibration data
[20250404_172353.]: ### Starting with regression calculations ###
[20250404_172353.]: Entered 'regression_type1'-Function
[20250404_172354.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172354.]: Logging df_agg: CpG#1
[20250404_172354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172354.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_172354.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172354.]: Entered 'hyperbolic_regression'-Function
[20250404_172354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172354.]: Entered 'cubic_regression'-Function
[20250404_172354.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172354.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172354.]: Logging df_agg: CpG#2
[20250404_172354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172354.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_172354.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172354.]: Entered 'hyperbolic_regression'-Function
[20250404_172354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: Entered 'cubic_regression'-Function
[20250404_172355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172355.]: Logging df_agg: CpG#3
[20250404_172355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172355.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_172355.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172355.]: Entered 'hyperbolic_regression'-Function
[20250404_172355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: Entered 'cubic_regression'-Function
[20250404_172355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172355.]: Logging df_agg: CpG#4
[20250404_172355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172355.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_172355.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172355.]: Entered 'hyperbolic_regression'-Function
[20250404_172355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: Entered 'cubic_regression'-Function
[20250404_172355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172355.]: Logging df_agg: CpG#5
[20250404_172355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172355.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_172355.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172355.]: Entered 'hyperbolic_regression'-Function
[20250404_172355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: Entered 'cubic_regression'-Function
[20250404_172355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172354.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172355.]: Logging df_agg: CpG#6
[20250404_172355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172355.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_172355.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172355.]: Entered 'hyperbolic_regression'-Function
[20250404_172355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: Entered 'cubic_regression'-Function
[20250404_172355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172355.]: Logging df_agg: CpG#7
[20250404_172355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172355.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_172355.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172355.]: Entered 'hyperbolic_regression'-Function
[20250404_172355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: Entered 'cubic_regression'-Function
[20250404_172355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172355.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172355.]: Logging df_agg: CpG#8
[20250404_172355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172355.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_172355.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172355.]: Entered 'hyperbolic_regression'-Function
[20250404_172355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172356.]: Entered 'cubic_regression'-Function
[20250404_172356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172356.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172356.]: Logging df_agg: CpG#9
[20250404_172356.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172356.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_172356.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172356.]: Entered 'hyperbolic_regression'-Function
[20250404_172356.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172356.]: Entered 'cubic_regression'-Function
[20250404_172356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172356.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172356.]: Logging df_agg: row_means
[20250404_172356.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172356.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_172356.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172356.]: Entered 'hyperbolic_regression'-Function
[20250404_172356.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172356.]: Entered 'cubic_regression'-Function
[20250404_172356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172359.]: Entered 'regression_type1'-Function
[20250404_172359.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172400.]: Logging df_agg: CpG#1
[20250404_172400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172400.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_172400.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_172400.]: Entered 'hyperbolic_regression'-Function
[20250404_172400.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172400.]: Entered 'cubic_regression'-Function
[20250404_172400.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172400.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172400.]: Logging df_agg: CpG#2
[20250404_172400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172400.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_172400.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_172400.]: Entered 'hyperbolic_regression'-Function
[20250404_172400.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172400.]: Entered 'cubic_regression'-Function
[20250404_172400.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172400.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172400.]: Logging df_agg: CpG#3
[20250404_172400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172400.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_172400.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_172400.]: Entered 'hyperbolic_regression'-Function
[20250404_172400.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: Entered 'cubic_regression'-Function
[20250404_172401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172401.]: Logging df_agg: CpG#4
[20250404_172401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172401.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_172401.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_172401.]: Entered 'hyperbolic_regression'-Function
[20250404_172401.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: Entered 'cubic_regression'-Function
[20250404_172401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172401.]: Logging df_agg: CpG#5
[20250404_172401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172401.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_172401.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_172401.]: Entered 'hyperbolic_regression'-Function
[20250404_172401.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: Entered 'cubic_regression'-Function
[20250404_172401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172400.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172400.]: Logging df_agg: CpG#6
[20250404_172400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172400.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_172400.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_172400.]: Entered 'hyperbolic_regression'-Function
[20250404_172400.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172400.]: Entered 'cubic_regression'-Function
[20250404_172400.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172401.]: Logging df_agg: CpG#7
[20250404_172401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172401.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_172401.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_172401.]: Entered 'hyperbolic_regression'-Function
[20250404_172401.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: Entered 'cubic_regression'-Function
[20250404_172401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172401.]: Logging df_agg: CpG#8
[20250404_172401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172401.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_172401.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_172401.]: Entered 'hyperbolic_regression'-Function
[20250404_172401.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: Entered 'cubic_regression'-Function
[20250404_172401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172401.]: Logging df_agg: CpG#9
[20250404_172401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172401.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_172401.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_172401.]: Entered 'hyperbolic_regression'-Function
[20250404_172401.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172401.]: Entered 'cubic_regression'-Function
[20250404_172401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172402.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172402.]: Logging df_agg: row_means
[20250404_172402.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172402.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_172402.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_172402.]: Entered 'hyperbolic_regression'-Function
[20250404_172402.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172402.]: Entered 'cubic_regression'-Function
[20250404_172402.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172403.]: Entered 'solving_equations'-Function
[20250404_172403.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_172403.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_172403.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_172403.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_172403.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_172403.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_172403.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_172403.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7851657015183
[20250404_172403.]: Samplename: 12.5
Root: 10.785
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.0727152156421
[20250404_172403.]: Samplename: 25
Root: 26.073
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.2074258210424
[20250404_172403.]: Samplename: 37.5
Root: 35.207
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.9305924748583
[20250404_172403.]: Samplename: 50
Root: 47.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.2847555363015
[20250404_172403.]: Samplename: 62.5
Root: 67.285
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.735332403378
[20250404_172403.]: Samplename: 75
Root: 75.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.1313047876192
[20250404_172403.]: Samplename: 87.5
Root: 84.131
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_172403.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_172403.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_172403.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_172403.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_172403.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_172403.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_172403.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_172403.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_172403.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_172403.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_172403.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_172403.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_172403.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_172403.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.36665146544904
[20250404_172403.]: Samplename: 12.5
Root: 8.367
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.0855280383989
[20250404_172403.]: Samplename: 25
Root: 23.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0098400819818
[20250404_172403.]: Samplename: 37.5
Root: 37.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.0085868408378
[20250404_172403.]: Samplename: 50
Root: 51.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.7441416833696
[20250404_172403.]: Samplename: 62.5
Root: 62.744
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.6857826005162
[20250404_172403.]: Samplename: 75
Root: 76.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.3046084696663
[20250404_172403.]: Samplename: 87.5
Root: 86.305
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_172403.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_172403.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_172403.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_172403.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_172403.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_172403.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_172403.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_172403.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_172403.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_172403.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_172403.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_172403.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_172403.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_172403.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.27736114274885
[20250404_172403.]: Samplename: 12.5
Root: 7.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.9863834890886
[20250404_172403.]: Samplename: 25
Root: 24.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0400823094579
[20250404_172403.]: Samplename: 37.5
Root: 34.04
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3077192847199
[20250404_172403.]: Samplename: 50
Root: 52.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0861558866387
[20250404_172403.]: Samplename: 62.5
Root: 65.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.3136588178128
[20250404_172403.]: Samplename: 75
Root: 78.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.058248740059
[20250404_172403.]: Samplename: 87.5
Root: 81.058
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_172403.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_172403.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_172403.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_172403.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_172403.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_172403.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_172403.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_172403.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_172403.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_172403.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_172403.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_172403.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_172403.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_172403.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_172403.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172403.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172403.]: ### Starting with regression calculations ###
[20250404_172403.]: Entered 'regression_type1'-Function
[20250404_172404.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_172404.]: Logging df_agg: CpG#1
[20250404_172404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172404.]: c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_172404.]: Entered 'hyperbolic_regression'-Function
[20250404_172404.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172404.]: Entered 'cubic_regression'-Function
[20250404_172404.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172404.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_172404.]: Logging df_agg: CpG#2
[20250404_172404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172404.]: c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_172404.]: Entered 'hyperbolic_regression'-Function
[20250404_172404.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172404.]: Entered 'cubic_regression'-Function
[20250404_172404.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172405.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_172405.]: Logging df_agg: CpG#3
[20250404_172405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172405.]: c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_172405.]: Entered 'hyperbolic_regression'-Function
[20250404_172405.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172405.]: Entered 'cubic_regression'-Function
[20250404_172405.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172405.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_172405.]: Logging df_agg: CpG#4
[20250404_172405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172405.]: c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_172405.]: Entered 'hyperbolic_regression'-Function
[20250404_172405.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172405.]: Entered 'cubic_regression'-Function
[20250404_172405.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172405.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_172405.]: Logging df_agg: CpG#5
[20250404_172405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172405.]: c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_172405.]: Entered 'hyperbolic_regression'-Function
[20250404_172405.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172405.]: Entered 'cubic_regression'-Function
[20250404_172405.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172404.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_172404.]: Logging df_agg: CpG#6
[20250404_172404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172404.]: c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_172404.]: Entered 'hyperbolic_regression'-Function
[20250404_172404.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172405.]: Entered 'cubic_regression'-Function
[20250404_172405.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172405.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_172405.]: Logging df_agg: CpG#7
[20250404_172405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172405.]: c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_172405.]: Entered 'hyperbolic_regression'-Function
[20250404_172405.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172405.]: Entered 'cubic_regression'-Function
[20250404_172405.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172406.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_172406.]: Logging df_agg: CpG#8
[20250404_172406.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172406.]: c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_172406.]: Entered 'hyperbolic_regression'-Function
[20250404_172406.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172406.]: Entered 'cubic_regression'-Function
[20250404_172406.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172406.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_172406.]: Logging df_agg: CpG#9
[20250404_172406.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172406.]: c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_172406.]: Entered 'hyperbolic_regression'-Function
[20250404_172406.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172406.]: Entered 'cubic_regression'-Function
[20250404_172406.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172406.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_172406.]: Logging df_agg: row_means
[20250404_172406.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172406.]: c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_172406.]: Entered 'hyperbolic_regression'-Function
[20250404_172406.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172407.]: Entered 'cubic_regression'-Function
[20250404_172407.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172407.]: Entered 'solving_equations'-Function
[20250404_172408.]: Solving cubic regression for CpG#1
Coefficients: 0Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.30533333333333Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_172408.]: Samplename: 12.5
Root: 10.279
--> Root in between the borders! Added to results.
Coefficients: -14.352Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_172408.]: Samplename: 25
Root: 21.591
--> Root in between the borders! Added to results.
Coefficients: -23.244Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_172408.]: Samplename: 37.5
Root: 36.617
--> Root in between the borders! Added to results.
Coefficients: -33.8645Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_172408.]: Samplename: 50
Root: 52.729
--> Root in between the borders! Added to results.
Coefficients: -45.318Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_172408.]: Samplename: 62.5
Root: 66.532
--> Root in between the borders! Added to results.
Coefficients: -54.857Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_172408.]: Samplename: 75
Root: 75.773
--> Root in between the borders! Added to results.
Coefficients: -62.062Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_172408.]: Samplename: 87.5
Root: 81.772
--> Root in between the borders! Added to results.
Coefficients: -90.01Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_172408.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172408.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172408.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172408.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172408.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172408.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172408.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172408.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172408.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172408.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172408.]: Solving cubic regression for CpG#3
Coefficients: 0Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.67Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_172408.]: Samplename: 12.5
Root: 9.387
--> Root in between the borders! Added to results.
Coefficients: -14.526Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_172408.]: Samplename: 25
Root: 24.373
--> Root in between the borders! Added to results.
Coefficients: -21.71Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_172408.]: Samplename: 37.5
Root: 36.135
--> Root in between the borders! Added to results.
Coefficients: -31.8725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_172408.]: Samplename: 50
Root: 51.29
--> Root in between the borders! Added to results.
Coefficients: -42.986Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_172408.]: Samplename: 62.5
Root: 65.561
--> Root in between the borders! Added to results.
Coefficients: -54.0725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_172408.]: Samplename: 75
Root: 77.683
--> Root in between the borders! Added to results.
Coefficients: -56.7533333333333Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_172408.]: Samplename: 87.5
Root: 80.348
--> Root in between the borders! Added to results.
Coefficients: -79.762Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_172408.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172408.]: Solving cubic regression for CpG#4
Coefficients: 0Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.65533333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_172408.]: Samplename: 12.5
Root: 11.333
--> Root in between the borders! Added to results.
Coefficients: -15.206Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_172408.]: Samplename: 25
Root: 22.933
--> Root in between the borders! Added to results.
Coefficients: -24.93Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_172408.]: Samplename: 37.5
Root: 37.542
--> Root in between the borders! Added to results.
Coefficients: -33.0395Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_172408.]: Samplename: 50
Root: 48.772
--> Root in between the borders! Added to results.
Coefficients: -49.658Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_172408.]: Samplename: 62.5
Root: 68.324
--> Root in between the borders! Added to results.
Coefficients: -55.942Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_172408.]: Samplename: 75
Root: 74.614
--> Root in between the borders! Added to results.
Coefficients: -64.9953333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_172408.]: Samplename: 87.5
Root: 82.816
--> Root in between the borders! Added to results.
Coefficients: -87.724Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_172408.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172408.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172408.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172408.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172408.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172408.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172408.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172408.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172408.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172408.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_172408.]: Solving cubic regression for CpG#6
Coefficients: 0Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.54266666666667Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_172408.]: Samplename: 12.5
Root: 11.495
--> Root in between the borders! Added to results.
Coefficients: -15.692Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_172408.]: Samplename: 25
Root: 26.346
--> Root in between the borders! Added to results.
Coefficients: -21.804Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_172408.]: Samplename: 37.5
Root: 35.332
--> Root in between the borders! Added to results.
Coefficients: -33.2485Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_172408.]: Samplename: 50
Root: 50.228
--> Root in between the borders! Added to results.
Coefficients: -46.704Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_172408.]: Samplename: 62.5
Root: 65.055
--> Root in between the borders! Added to results.
Coefficients: -55.636Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_172408.]: Samplename: 75
Root: 73.641
--> Root in between the borders! Added to results.
Coefficients: -71.3493333333333Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_172408.]: Samplename: 87.5
Root: 86.903
--> Root in between the borders! Added to results.
Coefficients: -89.46Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_172408.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172408.]: Solving cubic regression for CpG#7
Coefficients: 0Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.18066666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_172408.]: Samplename: 12.5
Root: 8.108
--> Root in between the borders! Added to results.
Coefficients: -10.05Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_172408.]: Samplename: 25
Root: 21.288
--> Root in between the borders! Added to results.
Coefficients: -16.236Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_172408.]: Samplename: 37.5
Root: 36.173
--> Root in between the borders! Added to results.
Coefficients: -24.8165Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_172408.]: Samplename: 50
Root: 54.247
--> Root in between the borders! Added to results.
Coefficients: -32.75Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_172408.]: Samplename: 62.5
Root: 67.087
--> Root in between the borders! Added to results.
Coefficients: -39.954Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_172408.]: Samplename: 75
Root: 76.377
--> Root in between the borders! Added to results.
Coefficients: -42.9206666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_172408.]: Samplename: 87.5
Root: 79.728
--> Root in between the borders! Added to results.
Coefficients: -66.008Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_172408.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172408.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172408.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172408.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172408.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172408.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172408.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172408.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172408.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172408.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_172408.]: Solving cubic regression for CpG#9
Coefficients: 0Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_172408.]: Samplename: 12.5
Root: 8.93
--> Root in between the borders! Added to results.
Coefficients: -13.716Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_172408.]: Samplename: 25
Root: 24.492
--> Root in between the borders! Added to results.
Coefficients: -19.634Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_172408.]: Samplename: 37.5
Root: 35.53
--> Root in between the borders! Added to results.
Coefficients: -30.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_172408.]: Samplename: 50
Root: 52.349
--> Root in between the borders! Added to results.
Coefficients: -41.696Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_172408.]: Samplename: 62.5
Root: 65.528
--> Root in between the borders! Added to results.
Coefficients: -51.9135Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_172408.]: Samplename: 75
Root: 74.87
--> Root in between the borders! Added to results.
Coefficients: -64.5026666666667Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_172408.]: Samplename: 87.5
Root: 84.256
--> Root in between the borders! Added to results.
Coefficients: -92Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_172408.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172408.]: Solving cubic regression for row_means
Coefficients: 0Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_172408.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.70125925925926Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_172408.]: Samplename: 12.5
Root: 9.866
--> Root in between the borders! Added to results.
Coefficients: -14.126Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_172408.]: Samplename: 25
Root: 24.413
--> Root in between the borders! Added to results.
Coefficients: -21.378Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_172408.]: Samplename: 37.5
Root: 36.177
--> Root in between the borders! Added to results.
Coefficients: -31.7547777777778Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_172408.]: Samplename: 50
Root: 51.091
--> Root in between the borders! Added to results.
Coefficients: -43.9506666666667Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_172408.]: Samplename: 62.5
Root: 65.785
--> Root in between the borders! Added to results.
Coefficients: -53.632Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_172408.]: Samplename: 75
Root: 75.683
--> Root in between the borders! Added to results.
Coefficients: -61.6601481481482Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_172408.]: Samplename: 87.5
Root: 82.966
--> Root in between the borders! Added to results.
Coefficients: -83.9631111111111Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_172408.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172408.]: ### Starting with regression calculations ###
[20250404_172408.]: Entered 'regression_type1'-Function
[20250404_172409.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_172409.]: Logging df_agg: CpG#1
[20250404_172409.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172409.]: c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_172409.]: Entered 'hyperbolic_regression'-Function
[20250404_172409.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172410.]: Entered 'cubic_regression'-Function
[20250404_172410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172410.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_172410.]: Logging df_agg: CpG#2
[20250404_172410.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172410.]: c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_172410.]: Entered 'hyperbolic_regression'-Function
[20250404_172410.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172410.]: Entered 'cubic_regression'-Function
[20250404_172410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172410.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_172410.]: Logging df_agg: CpG#3
[20250404_172410.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172410.]: c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_172410.]: Entered 'hyperbolic_regression'-Function
[20250404_172410.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: Entered 'cubic_regression'-Function
[20250404_172411.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_172411.]: Logging df_agg: CpG#4
[20250404_172411.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172411.]: c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_172411.]: Entered 'hyperbolic_regression'-Function
[20250404_172411.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: Entered 'cubic_regression'-Function
[20250404_172411.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_172411.]: Logging df_agg: CpG#5
[20250404_172411.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172411.]: c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_172411.]: Entered 'hyperbolic_regression'-Function
[20250404_172411.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: Entered 'cubic_regression'-Function
[20250404_172411.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172409.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_172410.]: Logging df_agg: CpG#6
[20250404_172410.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172410.]: c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_172410.]: Entered 'hyperbolic_regression'-Function
[20250404_172410.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172410.]: Entered 'cubic_regression'-Function
[20250404_172410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172410.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_172410.]: Logging df_agg: CpG#7
[20250404_172410.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172410.]: c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_172410.]: Entered 'hyperbolic_regression'-Function
[20250404_172410.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172410.]: Entered 'cubic_regression'-Function
[20250404_172410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172410.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_172410.]: Logging df_agg: CpG#8
[20250404_172410.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172410.]: c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_172410.]: Entered 'hyperbolic_regression'-Function
[20250404_172410.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: Entered 'cubic_regression'-Function
[20250404_172411.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_172411.]: Logging df_agg: CpG#9
[20250404_172411.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172411.]: c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_172411.]: Entered 'hyperbolic_regression'-Function
[20250404_172411.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: Entered 'cubic_regression'-Function
[20250404_172411.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_172411.]: Logging df_agg: row_means
[20250404_172411.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_172411.]: c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_172411.]: Entered 'hyperbolic_regression'-Function
[20250404_172411.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172411.]: Entered 'cubic_regression'-Function
[20250404_172411.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_172412.]: Entered 'solving_equations'-Function
[20250404_172412.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 78.9856894800976
[20250404_172412.]: Samplename: Sample#1
Root: 78.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 31.2695317984092
[20250404_172412.]: Samplename: Sample#10
Root: 31.27
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.7015782380441
[20250404_172412.]: Samplename: Sample#2
Root: 42.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.8152127901709
[20250404_172412.]: Samplename: Sample#3
Root: 57.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.2334360674289
[20250404_172412.]: Samplename: Sample#4
Root: 11.233
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.5293831001518
[20250404_172412.]: Samplename: Sample#5
Root: 23.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7706743072545
[20250404_172412.]: Samplename: Sample#6
Root: 24.771
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.3953425213349
[20250404_172412.]: Samplename: Sample#7
Root: 46.395
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.45071436915
[20250404_172412.]: Samplename: Sample#8
Root: 84.451
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.41337105576252
[20250404_172412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.413
--> '-10 < root < 0' --> substitute 0
[20250404_172412.]: Solving cubic regression for CpG#2
Coefficients: -59.7333333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#1
Root: 76.346
--> Root in between the borders! Added to results.
Coefficients: -19.048Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#10
Root: 31.371
--> Root in between the borders! Added to results.
Coefficients: -27.8783333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#2
Root: 43.142
--> Root in between the borders! Added to results.
Coefficients: -41.795Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#3
Root: 59.121
--> Root in between the borders! Added to results.
Coefficients: -2.21Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#4
Root: 4.128
--> Root in between the borders! Added to results.
Coefficients: -11.665Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#5
Root: 20.292
--> Root in between the borders! Added to results.
Coefficients: -10.08Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#6
Root: 17.745
--> Root in between the borders! Added to results.
Coefficients: -26.488Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#7
Root: 41.383
--> Root in between the borders! Added to results.
Coefficients: -70.532Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#8
Root: 85.378
--> Root in between the borders! Added to results.
Coefficients: -1.13Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172412.]: Samplename: Sample#9
Root: 2.127
--> Root in between the borders! Added to results.
[20250404_172412.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.5474014641742
[20250404_172412.]: Samplename: Sample#1
Root: 74.547
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3579002775045
[20250404_172412.]: Samplename: Sample#10
Root: 28.358
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.6085496577593
[20250404_172412.]: Samplename: Sample#2
Root: 42.609
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.3286114696456
[20250404_172412.]: Samplename: Sample#3
Root: 56.329
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.99034441243248
[20250404_172412.]: Samplename: Sample#4
Root: 7.99
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7023143744962
[20250404_172412.]: Samplename: Sample#5
Root: 24.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.8868798900698
[20250404_172412.]: Samplename: Sample#6
Root: 26.887
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.8318233973603
[20250404_172412.]: Samplename: Sample#7
Root: 44.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6737871528405
[20250404_172412.]: Samplename: Sample#8
Root: 84.674
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.26200732612128
[20250404_172412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.262
--> '-10 < root < 0' --> substitute 0
[20250404_172412.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 75.8433680333876
[20250404_172412.]: Samplename: Sample#1
Root: 75.843
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.0603248948201
[20250404_172412.]: Samplename: Sample#10
Root: 29.06
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0355928114108
[20250404_172412.]: Samplename: Sample#2
Root: 44.036
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.7751115686327
[20250404_172412.]: Samplename: Sample#3
Root: 58.775
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0319154866029
[20250404_172412.]: Samplename: Sample#4
Root: 11.032
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.9948971650737
[20250404_172412.]: Samplename: Sample#5
Root: 22.995
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.9415139419957
[20250404_172412.]: Samplename: Sample#6
Root: 27.942
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.4874049425657
[20250404_172412.]: Samplename: Sample#7
Root: 42.487
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6802730343613
[20250404_172412.]: Samplename: Sample#8
Root: 84.68
--> Root in between the borders! Added to results.
Hyperbolic solved: 3.00887785677921
[20250404_172412.]: Samplename: Sample#9
Root: 3.009
--> Root in between the borders! Added to results.
[20250404_172412.]: Solving cubic regression for CpG#5
Coefficients: -47.8373333333333Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -13.588Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#10
Root: 27.212
--> Root in between the borders! Added to results.
Coefficients: -25.3211428571429Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#2
Root: 44.85
--> Root in between the borders! Added to results.
Coefficients: -32.064Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#3
Root: 53.741
--> Root in between the borders! Added to results.
Coefficients: -4.074Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#4
Root: 9.444
--> Root in between the borders! Added to results.
Coefficients: -11.434Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#5
Root: 23.55
--> Root in between the borders! Added to results.
Coefficients: -13.294Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#6
Root: 26.722
--> Root in between the borders! Added to results.
Coefficients: -24.288Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#7
Root: 43.42
--> Root in between the borders! Added to results.
Coefficients: -63.134Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#8
Root: 88.215
--> Root in between the borders! Added to results.
Coefficients: 0.0360000000000005Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.091
--> '-10 < root < 0' --> substitute 0
[20250404_172412.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2200555510382
[20250404_172412.]: Samplename: Sample#1
Root: 79.22
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2526528381147
[20250404_172412.]: Samplename: Sample#10
Root: 30.253
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.9196854329573
[20250404_172412.]: Samplename: Sample#2
Root: 41.92
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8984354098215
[20250404_172412.]: Samplename: Sample#3
Root: 56.898
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.81576403111374
[20250404_172412.]: Samplename: Sample#4
Root: 8.816
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.6921622783918
[20250404_172412.]: Samplename: Sample#5
Root: 18.692
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9815019073132
[20250404_172412.]: Samplename: Sample#6
Root: 29.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8875178508205
[20250404_172412.]: Samplename: Sample#7
Root: 42.888
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.6303733181195
[20250404_172412.]: Samplename: Sample#8
Root: 86.63
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.38997712955107
[20250404_172412.]: Samplename: Sample#9
Root: 1.39
--> Root in between the borders! Added to results.
[20250404_172412.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 77.5278331978133
[20250404_172412.]: Samplename: Sample#1
Root: 77.528
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.0895401031897
[20250404_172412.]: Samplename: Sample#10
Root: 27.09
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.4382794903846
[20250404_172412.]: Samplename: Sample#2
Root: 48.438
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.8815971416453
[20250404_172412.]: Samplename: Sample#3
Root: 58.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.3295768294236
[20250404_172412.]: Samplename: Sample#4
Root: 13.33
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9816196357542
[20250404_172412.]: Samplename: Sample#5
Root: 26.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.9612159665911
[20250404_172412.]: Samplename: Sample#6
Root: 30.961
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.7456547820365
[20250404_172412.]: Samplename: Sample#7
Root: 45.746
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6033538318025
[20250404_172412.]: Samplename: Sample#8
Root: 84.603
--> Root in between the borders! Added to results.
Hyperbolic solved: -2.87380061592101
[20250404_172412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.874
--> '-10 < root < 0' --> substitute 0
[20250404_172412.]: Solving cubic regression for CpG#8
Coefficients: -55.3573333333333Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#1
Root: 72.421
--> Root in between the borders! Added to results.
Coefficients: -17.574Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#10
Root: 28.533
--> Root in between the borders! Added to results.
Coefficients: -22.9425714285714Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#2
Root: 35.766
--> Root in between the borders! Added to results.
Coefficients: -42.849Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#3
Root: 59.36
--> Root in between the borders! Added to results.
Coefficients: -4.604Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#4
Root: 8.481
--> Root in between the borders! Added to results.
Coefficients: -11.389Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#5
Root: 19.519
--> Root in between the borders! Added to results.
Coefficients: -25.784Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#6
Root: 39.413
--> Root in between the borders! Added to results.
Coefficients: -30.746Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#7
Root: 45.53
--> Root in between the borders! Added to results.
Coefficients: -66.912Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#8
Root: 83.654
--> Root in between the borders! Added to results.
Coefficients: 3.176Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -6.535
--> '-10 < root < 0' --> substitute 0
[20250404_172413.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 80.5486410672961
[20250404_172413.]: Samplename: Sample#1
Root: 80.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.810468482135
[20250404_172413.]: Samplename: Sample#10
Root: 27.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.2641649294309
[20250404_172413.]: Samplename: Sample#2
Root: 46.264
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.1903653427228
[20250404_172413.]: Samplename: Sample#3
Root: 57.19
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.63886339746086
[20250404_172413.]: Samplename: Sample#4
Root: 8.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2162393845509
[20250404_172413.]: Samplename: Sample#5
Root: 24.216
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.6394430638471
[20250404_172413.]: Samplename: Sample#6
Root: 39.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.3080887012493
[20250404_172413.]: Samplename: Sample#7
Root: 44.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.3259098830063
[20250404_172413.]: Samplename: Sample#8
Root: 87.326
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.17959639730045
[20250404_172413.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.18
--> '-10 < root < 0' --> substitute 0
[20250404_172413.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 76.7568961192102
[20250404_172413.]: Samplename: Sample#1
Root: 76.757
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.8326630603664
[20250404_172413.]: Samplename: Sample#10
Root: 28.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.0145327025204
[20250404_172413.]: Samplename: Sample#2
Root: 43.015
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.6144798147902
[20250404_172413.]: Samplename: Sample#3
Root: 57.614
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.86517972238162
[20250404_172413.]: Samplename: Sample#4
Root: 8.865
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.1849817550475
[20250404_172413.]: Samplename: Sample#5
Root: 22.185
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.1973843238972
[20250404_172413.]: Samplename: Sample#6
Root: 29.197
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.9174258632975
[20250404_172413.]: Samplename: Sample#7
Root: 43.917
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6607695784409
[20250404_172413.]: Samplename: Sample#8
Root: 85.661
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.551158207550385
[20250404_172413.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.551
--> '-10 < root < 0' --> substitute 0
[20250404_172413.]: Entered 'solving_equations'-Function
[20250404_172413.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_172413.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_172413.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_172413.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_172413.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_172413.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_172413.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_172413.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172413.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172413.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172413.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172413.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172413.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172413.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172413.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172413.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_172413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172413.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_172413.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_172413.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_172413.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_172413.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_172413.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_172413.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_172413.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172413.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_172413.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_172413.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_172413.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_172413.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_172413.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_172413.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_172413.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172413.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172413.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172413.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172413.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172413.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172413.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172413.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172413.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_172413.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_172413.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_172413.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_172413.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_172413.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_172413.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_172413.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_172413.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_172413.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172413.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_172413.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_172413.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_172413.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_172413.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_172413.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_172413.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_172413.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172413.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172413.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172413.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172413.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172413.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172413.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172413.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172413.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_172413.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_172413.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_172413.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_172413.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_172413.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_172413.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_172413.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_172413.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_172413.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_172413.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_172413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_172413.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_172413.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_172413.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_172413.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_172413.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_172413.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_172413.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_172413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[20250404_172452.]: Entered 'clean_dt'-Function
[20250404_172452.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172452.]: got experimental data
[20250404_172452.]: Entered 'clean_dt'-Function
[20250404_172452.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data)
[20250404_172452.]: got experimental data
[20250404_172452.]: Entered 'clean_dt'-Function
[20250404_172452.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172452.]: got calibration data
[20250404_172452.]: Entered 'clean_dt'-Function
[20250404_172452.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172452.]: got calibration data
[20250404_172452.]: Entered 'hyperbolic_regression'-Function
[20250404_172452.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• On CRAN (4): 'test-algorithm_minmax_FALSE.R:80:5',
'test-algorithm_minmax_TRUE.R:76:5', 'test-hyperbolic.R:27:5',
'test-lints.R:12:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-algorithm_minmax_FALSE_re.R:170:5'): algorithm test, type 1, minmax = FALSE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_FALSE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-algorithm_minmax_TRUE_re.R:170:5'): algorithm test, type 1, minmax = TRUE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_TRUE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:17:5'): test normal function of file import of type 1 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:17:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:65:5'): test normal function of file import of type 2 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:65:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-create_aggregated.R:19:5'): test functioning of aggregated function ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-create_aggregated.R:19:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
Error: Test failures
Execution halted
Error in deferred_run(env) : could not find function "deferred_run"
Calls: <Anonymous>
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.3.4
Check: tests
Result: ERROR
Running ‘testthat.R’ [4m/10m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rBiasCorrection)
>
> local_edition(3)
>
> test_check("rBiasCorrection")
[20250404_171553.]: Entered 'clean_dt'-Function
[20250404_171553.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_171553.]: got experimental data
[20250404_171553.]: Entered 'clean_dt'-Function
[20250404_171553.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_171553.]: got calibration data
[20250404_171553.]: ### Starting with regression calculations ###
[20250404_171553.]: Entered 'regression_type1'-Function
[20250404_171554.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171555.]: Logging df_agg: CpG#1
[20250404_171555.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171555.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_171555.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171555.]: Entered 'hyperbolic_regression'-Function
[20250404_171555.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171556.]: Entered 'cubic_regression'-Function
[20250404_171556.]: 'cubic_regression': minmax = FALSE
[20250404_171556.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171556.]: Logging df_agg: CpG#2
[20250404_171556.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171556.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_171556.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171556.]: Entered 'hyperbolic_regression'-Function
[20250404_171556.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171557.]: Entered 'cubic_regression'-Function
[20250404_171557.]: 'cubic_regression': minmax = FALSE
[20250404_171557.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171557.]: Logging df_agg: CpG#3
[20250404_171557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171557.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_171557.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171557.]: Entered 'hyperbolic_regression'-Function
[20250404_171557.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171558.]: Entered 'cubic_regression'-Function
[20250404_171558.]: 'cubic_regression': minmax = FALSE
[20250404_171558.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171558.]: Logging df_agg: CpG#4
[20250404_171558.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171558.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_171558.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171558.]: Entered 'hyperbolic_regression'-Function
[20250404_171558.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171559.]: Entered 'cubic_regression'-Function
[20250404_171559.]: 'cubic_regression': minmax = FALSE
[20250404_171559.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171559.]: Logging df_agg: CpG#5
[20250404_171559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171559.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_171559.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171559.]: Entered 'hyperbolic_regression'-Function
[20250404_171559.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171600.]: Entered 'cubic_regression'-Function
[20250404_171601.]: 'cubic_regression': minmax = FALSE
[20250404_171556.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171557.]: Logging df_agg: CpG#6
[20250404_171557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171557.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_171557.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171557.]: Entered 'hyperbolic_regression'-Function
[20250404_171557.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171558.]: Entered 'cubic_regression'-Function
[20250404_171558.]: 'cubic_regression': minmax = FALSE
[20250404_171558.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171558.]: Logging df_agg: CpG#7
[20250404_171558.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171558.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_171558.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171558.]: Entered 'hyperbolic_regression'-Function
[20250404_171558.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171559.]: Entered 'cubic_regression'-Function
[20250404_171559.]: 'cubic_regression': minmax = FALSE
[20250404_171559.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171559.]: Logging df_agg: CpG#8
[20250404_171559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171559.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_171559.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171559.]: Entered 'hyperbolic_regression'-Function
[20250404_171559.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171600.]: Entered 'cubic_regression'-Function
[20250404_171600.]: 'cubic_regression': minmax = FALSE
[20250404_171600.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171600.]: Logging df_agg: CpG#9
[20250404_171600.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171600.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_171600.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171600.]: Entered 'hyperbolic_regression'-Function
[20250404_171600.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171601.]: Entered 'cubic_regression'-Function
[20250404_171601.]: 'cubic_regression': minmax = FALSE
[20250404_171601.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171601.]: Logging df_agg: row_means
[20250404_171601.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171601.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_171601.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171601.]: Entered 'hyperbolic_regression'-Function
[20250404_171601.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171602.]: Entered 'cubic_regression'-Function
[20250404_171602.]: 'cubic_regression': minmax = FALSE
[20250404_171613.]: Entered 'regression_type1'-Function
[20250404_171615.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171616.]: Logging df_agg: CpG#1
[20250404_171616.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171616.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_171616.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171616.]: Entered 'hyperbolic_regression'-Function
[20250404_171616.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171617.]: Entered 'cubic_regression'-Function
[20250404_171617.]: 'cubic_regression': minmax = FALSE
[20250404_171617.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171617.]: Logging df_agg: CpG#2
[20250404_171617.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171617.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_171617.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171617.]: Entered 'hyperbolic_regression'-Function
[20250404_171617.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171618.]: Entered 'cubic_regression'-Function
[20250404_171618.]: 'cubic_regression': minmax = FALSE
[20250404_171618.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171618.]: Logging df_agg: CpG#3
[20250404_171618.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171618.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_171618.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171618.]: Entered 'hyperbolic_regression'-Function
[20250404_171618.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171620.]: Entered 'cubic_regression'-Function
[20250404_171620.]: 'cubic_regression': minmax = FALSE
[20250404_171620.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171620.]: Logging df_agg: CpG#4
[20250404_171620.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171620.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_171620.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171620.]: Entered 'hyperbolic_regression'-Function
[20250404_171620.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171621.]: Entered 'cubic_regression'-Function
[20250404_171621.]: 'cubic_regression': minmax = FALSE
[20250404_171621.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171621.]: Logging df_agg: CpG#5
[20250404_171621.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171621.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_171621.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171621.]: Entered 'hyperbolic_regression'-Function
[20250404_171621.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171622.]: Entered 'cubic_regression'-Function
[20250404_171622.]: 'cubic_regression': minmax = FALSE
[20250404_171615.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171616.]: Logging df_agg: CpG#6
[20250404_171616.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171616.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_171616.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171616.]: Entered 'hyperbolic_regression'-Function
[20250404_171616.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171618.]: Entered 'cubic_regression'-Function
[20250404_171618.]: 'cubic_regression': minmax = FALSE
[20250404_171618.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171618.]: Logging df_agg: CpG#7
[20250404_171618.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171618.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_171618.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171618.]: Entered 'hyperbolic_regression'-Function
[20250404_171618.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171619.]: Entered 'cubic_regression'-Function
[20250404_171619.]: 'cubic_regression': minmax = FALSE
[20250404_171619.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171619.]: Logging df_agg: CpG#8
[20250404_171619.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171619.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_171619.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171619.]: Entered 'hyperbolic_regression'-Function
[20250404_171619.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171620.]: Entered 'cubic_regression'-Function
[20250404_171620.]: 'cubic_regression': minmax = FALSE
[20250404_171621.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171621.]: Logging df_agg: CpG#9
[20250404_171621.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171621.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_171621.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171621.]: Entered 'hyperbolic_regression'-Function
[20250404_171621.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171622.]: Entered 'cubic_regression'-Function
[20250404_171622.]: 'cubic_regression': minmax = FALSE
[20250404_171622.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171622.]: Logging df_agg: row_means
[20250404_171622.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171622.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_171622.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171622.]: Entered 'hyperbolic_regression'-Function
[20250404_171622.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171623.]: Entered 'cubic_regression'-Function
[20250404_171623.]: 'cubic_regression': minmax = FALSE
[20250404_171628.]: Entered 'clean_dt'-Function
[20250404_171628.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_171628.]: got experimental data
[20250404_171628.]: Entered 'clean_dt'-Function
[20250404_171629.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_171629.]: got calibration data
[20250404_171629.]: ### Starting with regression calculations ###
[20250404_171629.]: Entered 'regression_type1'-Function
[20250404_171630.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171630.]: Logging df_agg: CpG#1
[20250404_171630.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171630.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_171630.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171630.]: Entered 'hyperbolic_regression'-Function
[20250404_171630.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171631.]: Entered 'cubic_regression'-Function
[20250404_171631.]: 'cubic_regression': minmax = FALSE
[20250404_171631.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171631.]: Logging df_agg: CpG#2
[20250404_171631.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171631.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_171631.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171631.]: Entered 'hyperbolic_regression'-Function
[20250404_171631.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171632.]: Entered 'cubic_regression'-Function
[20250404_171632.]: 'cubic_regression': minmax = FALSE
[20250404_171632.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171632.]: Logging df_agg: CpG#3
[20250404_171632.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171632.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_171632.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171632.]: Entered 'hyperbolic_regression'-Function
[20250404_171632.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171633.]: Entered 'cubic_regression'-Function
[20250404_171633.]: 'cubic_regression': minmax = FALSE
[20250404_171633.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171633.]: Logging df_agg: CpG#4
[20250404_171633.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171633.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_171633.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171633.]: Entered 'hyperbolic_regression'-Function
[20250404_171633.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171635.]: Entered 'cubic_regression'-Function
[20250404_171635.]: 'cubic_regression': minmax = FALSE
[20250404_171635.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171635.]: Logging df_agg: CpG#5
[20250404_171635.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171635.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_171635.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171635.]: Entered 'hyperbolic_regression'-Function
[20250404_171635.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171636.]: Entered 'cubic_regression'-Function
[20250404_171636.]: 'cubic_regression': minmax = FALSE
[20250404_171632.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171632.]: Logging df_agg: CpG#6
[20250404_171632.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171632.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_171632.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171632.]: Entered 'hyperbolic_regression'-Function
[20250404_171632.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171633.]: Entered 'cubic_regression'-Function
[20250404_171633.]: 'cubic_regression': minmax = FALSE
[20250404_171633.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171633.]: Logging df_agg: CpG#7
[20250404_171633.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171633.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_171633.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171633.]: Entered 'hyperbolic_regression'-Function
[20250404_171633.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171635.]: Entered 'cubic_regression'-Function
[20250404_171635.]: 'cubic_regression': minmax = FALSE
[20250404_171635.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171635.]: Logging df_agg: CpG#8
[20250404_171635.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171635.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_171635.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171635.]: Entered 'hyperbolic_regression'-Function
[20250404_171635.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171637.]: Entered 'cubic_regression'-Function
[20250404_171637.]: 'cubic_regression': minmax = FALSE
[20250404_171637.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171637.]: Logging df_agg: CpG#9
[20250404_171637.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171637.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_171637.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171637.]: Entered 'hyperbolic_regression'-Function
[20250404_171637.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171638.]: Entered 'cubic_regression'-Function
[20250404_171638.]: 'cubic_regression': minmax = FALSE
[20250404_171638.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171638.]: Logging df_agg: row_means
[20250404_171638.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171638.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_171638.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171638.]: Entered 'hyperbolic_regression'-Function
[20250404_171638.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171639.]: Entered 'cubic_regression'-Function
[20250404_171639.]: 'cubic_regression': minmax = FALSE
[20250404_171648.]: Entered 'regression_type1'-Function
[20250404_171650.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171651.]: Logging df_agg: CpG#1
[20250404_171651.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171651.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_171651.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171651.]: Entered 'hyperbolic_regression'-Function
[20250404_171651.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171652.]: Entered 'cubic_regression'-Function
[20250404_171652.]: 'cubic_regression': minmax = FALSE
[20250404_171652.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171652.]: Logging df_agg: CpG#2
[20250404_171652.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171652.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_171652.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171652.]: Entered 'hyperbolic_regression'-Function
[20250404_171652.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171653.]: Entered 'cubic_regression'-Function
[20250404_171653.]: 'cubic_regression': minmax = FALSE
[20250404_171653.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171653.]: Logging df_agg: CpG#3
[20250404_171653.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171653.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_171653.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171653.]: Entered 'hyperbolic_regression'-Function
[20250404_171653.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171654.]: Entered 'cubic_regression'-Function
[20250404_171654.]: 'cubic_regression': minmax = FALSE
[20250404_171654.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171654.]: Logging df_agg: CpG#4
[20250404_171654.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171654.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_171654.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171654.]: Entered 'hyperbolic_regression'-Function
[20250404_171654.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171656.]: Entered 'cubic_regression'-Function
[20250404_171656.]: 'cubic_regression': minmax = FALSE
[20250404_171656.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171656.]: Logging df_agg: CpG#5
[20250404_171656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171656.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_171656.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171656.]: Entered 'hyperbolic_regression'-Function
[20250404_171656.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171657.]: Entered 'cubic_regression'-Function
[20250404_171657.]: 'cubic_regression': minmax = FALSE
[20250404_171652.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171653.]: Logging df_agg: CpG#6
[20250404_171653.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171653.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_171653.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171653.]: Entered 'hyperbolic_regression'-Function
[20250404_171653.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171654.]: Entered 'cubic_regression'-Function
[20250404_171654.]: 'cubic_regression': minmax = FALSE
[20250404_171654.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171654.]: Logging df_agg: CpG#7
[20250404_171654.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171654.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_171654.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171654.]: Entered 'hyperbolic_regression'-Function
[20250404_171654.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171655.]: Entered 'cubic_regression'-Function
[20250404_171655.]: 'cubic_regression': minmax = FALSE
[20250404_171655.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171655.]: Logging df_agg: CpG#8
[20250404_171655.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171655.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_171655.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171655.]: Entered 'hyperbolic_regression'-Function
[20250404_171655.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171656.]: Entered 'cubic_regression'-Function
[20250404_171656.]: 'cubic_regression': minmax = FALSE
[20250404_171656.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171656.]: Logging df_agg: CpG#9
[20250404_171656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171656.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_171656.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171656.]: Entered 'hyperbolic_regression'-Function
[20250404_171656.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171657.]: Entered 'cubic_regression'-Function
[20250404_171657.]: 'cubic_regression': minmax = FALSE
[20250404_171657.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171657.]: Logging df_agg: row_means
[20250404_171657.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171657.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_171657.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171657.]: Entered 'hyperbolic_regression'-Function
[20250404_171657.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171658.]: Entered 'cubic_regression'-Function
[20250404_171658.]: 'cubic_regression': minmax = FALSE
[20250404_171702.]: Entered 'solving_equations'-Function
[20250404_171702.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_171702.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_171702.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_171702.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_171702.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_171702.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_171702.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_171702.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_171702.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_171702.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_171702.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 1.13660501904968
[20250404_171702.]: Samplename: 0
Root: 1.137
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.4129696733689
[20250404_171702.]: Samplename: 12.5
Root: 11.413
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.174000526428
[20250404_171702.]: Samplename: 25
Root: 26.174
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.1050449117028
[20250404_171702.]: Samplename: 37.5
Root: 35.105
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.685500330611
[20250404_171702.]: Samplename: 50
Root: 47.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.1440494417104
[20250404_171702.]: Samplename: 62.5
Root: 67.144
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7644668894086
[20250404_171702.]: Samplename: 75
Root: 75.764
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.4054158616395
[20250404_171702.]: Samplename: 87.5
Root: 84.405
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.94827248399
[20250404_171702.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.948
--> '100 < root < 110' --> substitute 100
[20250404_171702.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_171702.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_171702.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_171702.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_171702.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_171702.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_171702.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_171702.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_171702.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_171702.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_171702.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_171702.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_171702.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_171702.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_171702.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_171702.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_171702.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_171702.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_171702.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_171702.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_171702.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 2.41558626275183
[20250404_171702.]: Samplename: 0
Root: 2.416
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.1649674907454
[20250404_171702.]: Samplename: 12.5
Root: 10.165
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.9830820412762
[20250404_171702.]: Samplename: 25
Root: 23.983
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2773619900429
[20250404_171702.]: Samplename: 37.5
Root: 37.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.8659386543864
[20250404_171702.]: Samplename: 50
Root: 50.866
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.4342273571069
[20250404_171702.]: Samplename: 62.5
Root: 62.434
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.3915260534323
[20250404_171702.]: Samplename: 75
Root: 76.392
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.159788778566
[20250404_171702.]: Samplename: 87.5
Root: 86.16
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.267759893323
[20250404_171702.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.268
--> '100 < root < 110' --> substitute 100
[20250404_171702.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_171702.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_171702.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_171703.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_171703.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_171703.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_171703.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_171703.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_171703.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_171703.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_171703.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_171703.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_171703.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_171703.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_171703.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_171703.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_171703.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_171703.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_171703.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_171703.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_171703.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 2.80068218205093
[20250404_171703.]: Samplename: 0
Root: 2.801
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.27535134596596
[20250404_171703.]: Samplename: 12.5
Root: 9.275
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4762621928197
[20250404_171703.]: Samplename: 25
Root: 25.476
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0122075735416
[20250404_171703.]: Samplename: 37.5
Root: 34.012
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.7842655662325
[20250404_171703.]: Samplename: 50
Root: 51.784
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.6732311906145
[20250404_171703.]: Samplename: 62.5
Root: 64.673
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.4326978859189
[20250404_171703.]: Samplename: 75
Root: 78.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.3427232852719
[20250404_171703.]: Samplename: 87.5
Root: 81.343
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.964406640583
[20250404_171703.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.964
--> '100 < root < 110' --> substitute 100
[20250404_171703.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: -2.13403721845678
[20250404_171703.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.134
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.5082192457956
[20250404_171703.]: Samplename: 12.5
Root: 10.508
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9164567253388
[20250404_171703.]: Samplename: 25
Root: 26.916
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.8334779159501
[20250404_171703.]: Samplename: 37.5
Root: 36.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.0097895977263
[20250404_171703.]: Samplename: 50
Root: 52.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8930527921581
[20250404_171703.]: Samplename: 62.5
Root: 64.893
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.5671055499357
[20250404_171703.]: Samplename: 75
Root: 74.567
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.5294954832669
[20250404_171703.]: Samplename: 87.5
Root: 84.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.047146466811
[20250404_171703.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.047
--> '100 < root < 110' --> substitute 100
[20250404_171703.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_171703.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_171703.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_171703.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_171703.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_171703.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_171703.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_171703.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_171703.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_171703.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_171703.]: ### Starting with regression calculations ###
[20250404_171703.]: Entered 'regression_type1'-Function
[20250404_171705.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_171706.]: Logging df_agg: CpG#1
[20250404_171706.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171706.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_171706.]: Entered 'hyperbolic_regression'-Function
[20250404_171706.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171707.]: Entered 'cubic_regression'-Function
[20250404_171707.]: 'cubic_regression': minmax = FALSE
[20250404_171707.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_171707.]: Logging df_agg: CpG#2
[20250404_171707.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171707.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_171707.]: Entered 'hyperbolic_regression'-Function
[20250404_171707.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171708.]: Entered 'cubic_regression'-Function
[20250404_171708.]: 'cubic_regression': minmax = FALSE
[20250404_171708.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_171708.]: Logging df_agg: CpG#3
[20250404_171708.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171708.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_171708.]: Entered 'hyperbolic_regression'-Function
[20250404_171708.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171709.]: Entered 'cubic_regression'-Function
[20250404_171709.]: 'cubic_regression': minmax = FALSE
[20250404_171709.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_171709.]: Logging df_agg: CpG#4
[20250404_171709.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171709.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_171709.]: Entered 'hyperbolic_regression'-Function
[20250404_171709.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171709.]: Entered 'cubic_regression'-Function
[20250404_171710.]: 'cubic_regression': minmax = FALSE
[20250404_171710.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_171710.]: Logging df_agg: CpG#5
[20250404_171710.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171710.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_171710.]: Entered 'hyperbolic_regression'-Function
[20250404_171710.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171711.]: Entered 'cubic_regression'-Function
[20250404_171711.]: 'cubic_regression': minmax = FALSE
[20250404_171707.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_171707.]: Logging df_agg: CpG#6
[20250404_171707.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171707.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_171707.]: Entered 'hyperbolic_regression'-Function
[20250404_171707.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171708.]: Entered 'cubic_regression'-Function
[20250404_171708.]: 'cubic_regression': minmax = FALSE
[20250404_171708.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_171708.]: Logging df_agg: CpG#7
[20250404_171708.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171708.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_171708.]: Entered 'hyperbolic_regression'-Function
[20250404_171708.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171708.]: Entered 'cubic_regression'-Function
[20250404_171708.]: 'cubic_regression': minmax = FALSE
[20250404_171708.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_171708.]: Logging df_agg: CpG#8
[20250404_171708.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171708.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_171708.]: Entered 'hyperbolic_regression'-Function
[20250404_171708.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171709.]: Entered 'cubic_regression'-Function
[20250404_171709.]: 'cubic_regression': minmax = FALSE
[20250404_171709.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_171709.]: Logging df_agg: CpG#9
[20250404_171709.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171709.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_171709.]: Entered 'hyperbolic_regression'-Function
[20250404_171709.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171710.]: Entered 'cubic_regression'-Function
[20250404_171710.]: 'cubic_regression': minmax = FALSE
[20250404_171710.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_171710.]: Logging df_agg: row_means
[20250404_171710.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171710.]: c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_171710.]: Entered 'hyperbolic_regression'-Function
[20250404_171710.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171711.]: Entered 'cubic_regression'-Function
[20250404_171711.]: 'cubic_regression': minmax = FALSE
[20250404_171713.]: Entered 'solving_equations'-Function
[20250404_171713.]: Solving cubic regression for CpG#1
Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_171713.]: Samplename: 0
Root: 1.334
--> Root in between the borders! Added to results.
Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_171713.]: Samplename: 12.5
Root: 11.446
--> Root in between the borders! Added to results.
Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_171713.]: Samplename: 25
Root: 22.228
--> Root in between the borders! Added to results.
Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_171713.]: Samplename: 37.5
Root: 36.374
--> Root in between the borders! Added to results.
Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_171713.]: Samplename: 50
Root: 52.044
--> Root in between the borders! Added to results.
Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_171713.]: Samplename: 62.5
Root: 66.144
--> Root in between the borders! Added to results.
Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_171713.]: Samplename: 75
Root: 75.864
--> Root in between the borders! Added to results.
Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_171713.]: Samplename: 87.5
Root: 82.254
--> Root in between the borders! Added to results.
Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_171713.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.877
--> '100 < root < 110' --> substitute 100
[20250404_171713.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171713.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171713.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171713.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171713.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171713.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171713.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171713.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171713.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171713.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_171713.]: Solving cubic regression for CpG#3
Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_171713.]: Samplename: 0
Root: 1.441
--> Root in between the borders! Added to results.
Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_171713.]: Samplename: 12.5
Root: 10.568
--> Root in between the borders! Added to results.
Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_171713.]: Samplename: 25
Root: 24.796
--> Root in between the borders! Added to results.
Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_171713.]: Samplename: 37.5
Root: 35.952
--> Root in between the borders! Added to results.
Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_171713.]: Samplename: 50
Root: 50.684
--> Root in between the borders! Added to results.
Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_171713.]: Samplename: 62.5
Root: 65.142
--> Root in between the borders! Added to results.
Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_171713.]: Samplename: 75
Root: 77.905
--> Root in between the borders! Added to results.
Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_171713.]: Samplename: 87.5
Root: 80.767
--> Root in between the borders! Added to results.
Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_171713.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.38
--> '100 < root < 110' --> substitute 100
[20250404_171713.]: Solving cubic regression for CpG#4
Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_171713.]: Samplename: 0
Root: 0.858
--> Root in between the borders! Added to results.
Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_171713.]: Samplename: 12.5
Root: 12.086
--> Root in between the borders! Added to results.
Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_171713.]: Samplename: 25
Root: 23.316
--> Root in between the borders! Added to results.
Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_171713.]: Samplename: 37.5
Root: 37.383
--> Root in between the borders! Added to results.
Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_171713.]: Samplename: 50
Root: 48.353
--> Root in between the borders! Added to results.
Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_171713.]: Samplename: 62.5
Root: 68.082
--> Root in between the borders! Added to results.
Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_171713.]: Samplename: 75
Root: 74.615
--> Root in between the borders! Added to results.
Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_171713.]: Samplename: 87.5
Root: 83.254
--> Root in between the borders! Added to results.
Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_171713.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.715
--> '100 < root < 110' --> substitute 100
[20250404_171713.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171713.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171713.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171713.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171713.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171713.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171713.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171713.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171713.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171713.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_171713.]: Solving cubic regression for CpG#6
Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_171713.]: Samplename: 0
Root: 0.349
--> Root in between the borders! Added to results.
Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_171713.]: Samplename: 12.5
Root: 11.718
--> Root in between the borders! Added to results.
Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_171713.]: Samplename: 25
Root: 26.396
--> Root in between the borders! Added to results.
Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_171713.]: Samplename: 37.5
Root: 35.301
--> Root in between the borders! Added to results.
Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_171713.]: Samplename: 50
Root: 50.134
--> Root in between the borders! Added to results.
Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_171713.]: Samplename: 62.5
Root: 64.993
--> Root in between the borders! Added to results.
Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_171713.]: Samplename: 75
Root: 73.639
--> Root in between the borders! Added to results.
Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_171713.]: Samplename: 87.5
Root: 87.043
--> Root in between the borders! Added to results.
Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_171714.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.329
--> '100 < root < 110' --> substitute 100
[20250404_171714.]: Solving cubic regression for CpG#7
Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_171714.]: Samplename: 0
Root: 2.13
--> Root in between the borders! Added to results.
Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_171714.]: Samplename: 12.5
Root: 9.973
--> Root in between the borders! Added to results.
Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_171714.]: Samplename: 25
Root: 22.206
--> Root in between the borders! Added to results.
Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_171714.]: Samplename: 37.5
Root: 35.814
--> Root in between the borders! Added to results.
Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_171714.]: Samplename: 50
Root: 53.28
--> Root in between the borders! Added to results.
Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_171714.]: Samplename: 62.5
Root: 66.598
--> Root in between the borders! Added to results.
Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_171714.]: Samplename: 75
Root: 76.575
--> Root in between the borders! Added to results.
Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_171714.]: Samplename: 87.5
Root: 80.219
--> Root in between the borders! Added to results.
Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_171714.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.506
--> '100 < root < 110' --> substitute 100
[20250404_171714.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171714.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171714.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171714.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171714.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171714.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171714.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171714.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171714.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171714.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_171714.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171714.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171714.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171714.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171714.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171714.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171714.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171714.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171714.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171714.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_171714.]: Solving cubic regression for row_means
Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_171714.]: Samplename: 0
Root: 1.287
--> Root in between the borders! Added to results.
Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_171714.]: Samplename: 12.5
Root: 10.847
--> Root in between the borders! Added to results.
Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_171714.]: Samplename: 25
Root: 24.737
--> Root in between the borders! Added to results.
Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_171714.]: Samplename: 37.5
Root: 36.02
--> Root in between the borders! Added to results.
Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_171714.]: Samplename: 50
Root: 50.639
--> Root in between the borders! Added to results.
Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_171714.]: Samplename: 62.5
Root: 65.497
--> Root in between the borders! Added to results.
Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_171714.]: Samplename: 75
Root: 75.751
--> Root in between the borders! Added to results.
Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_171714.]: Samplename: 87.5
Root: 83.403
--> Root in between the borders! Added to results.
Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_171714.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.573
--> '100 < root < 110' --> substitute 100
[20250404_171714.]: ### Starting with regression calculations ###
[20250404_171714.]: Entered 'regression_type1'-Function
[20250404_171716.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_171717.]: Logging df_agg: CpG#1
[20250404_171717.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171717.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_171717.]: Entered 'hyperbolic_regression'-Function
[20250404_171717.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171719.]: Entered 'cubic_regression'-Function
[20250404_171719.]: 'cubic_regression': minmax = FALSE
[20250404_171719.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_171719.]: Logging df_agg: CpG#2
[20250404_171719.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171719.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_171719.]: Entered 'hyperbolic_regression'-Function
[20250404_171719.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171720.]: Entered 'cubic_regression'-Function
[20250404_171720.]: 'cubic_regression': minmax = FALSE
[20250404_171720.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_171720.]: Logging df_agg: CpG#3
[20250404_171720.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171720.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_171720.]: Entered 'hyperbolic_regression'-Function
[20250404_171720.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171721.]: Entered 'cubic_regression'-Function
[20250404_171721.]: 'cubic_regression': minmax = FALSE
[20250404_171721.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_171721.]: Logging df_agg: CpG#4
[20250404_171721.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171721.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_171721.]: Entered 'hyperbolic_regression'-Function
[20250404_171721.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171722.]: Entered 'cubic_regression'-Function
[20250404_171722.]: 'cubic_regression': minmax = FALSE
[20250404_171722.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_171722.]: Logging df_agg: CpG#5
[20250404_171722.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171722.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_171722.]: Entered 'hyperbolic_regression'-Function
[20250404_171722.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171723.]: Entered 'cubic_regression'-Function
[20250404_171723.]: 'cubic_regression': minmax = FALSE
[20250404_171717.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_171718.]: Logging df_agg: CpG#6
[20250404_171718.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171718.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_171718.]: Entered 'hyperbolic_regression'-Function
[20250404_171718.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171719.]: Entered 'cubic_regression'-Function
[20250404_171719.]: 'cubic_regression': minmax = FALSE
[20250404_171720.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_171720.]: Logging df_agg: CpG#7
[20250404_171720.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171720.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_171720.]: Entered 'hyperbolic_regression'-Function
[20250404_171720.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171720.]: Entered 'cubic_regression'-Function
[20250404_171720.]: 'cubic_regression': minmax = FALSE
[20250404_171721.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_171721.]: Logging df_agg: CpG#8
[20250404_171721.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171721.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_171721.]: Entered 'hyperbolic_regression'-Function
[20250404_171721.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171721.]: Entered 'cubic_regression'-Function
[20250404_171721.]: 'cubic_regression': minmax = FALSE
[20250404_171721.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_171721.]: Logging df_agg: CpG#9
[20250404_171721.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171721.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_171721.]: Entered 'hyperbolic_regression'-Function
[20250404_171721.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171722.]: Entered 'cubic_regression'-Function
[20250404_171723.]: 'cubic_regression': minmax = FALSE
[20250404_171723.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_171723.]: Logging df_agg: row_means
[20250404_171723.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171723.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_171723.]: Entered 'hyperbolic_regression'-Function
[20250404_171723.]: 'hyperbolic_regression': minmax = FALSE
[20250404_171724.]: Entered 'cubic_regression'-Function
[20250404_171724.]: 'cubic_regression': minmax = FALSE
[20250404_171726.]: Entered 'solving_equations'-Function
[20250404_171726.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 79.8673456895745
[20250404_171726.]: Samplename: Sample#1
Root: 79.867
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.7900184340805
[20250404_171726.]: Samplename: Sample#10
Root: 29.79
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.6525415639691
[20250404_171726.]: Samplename: Sample#2
Root: 41.653
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.4652090254513
[20250404_171726.]: Samplename: Sample#3
Root: 57.465
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.2007130627765
[20250404_171726.]: Samplename: Sample#4
Root: 9.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8059600538131
[20250404_171726.]: Samplename: Sample#5
Root: 21.806
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.083796735881
[20250404_171726.]: Samplename: Sample#6
Root: 23.084
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.5034245569385
[20250404_171726.]: Samplename: Sample#7
Root: 45.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6987904075704
[20250404_171726.]: Samplename: Sample#8
Root: 85.699
--> Root in between the borders! Added to results.
Hyperbolic solved: -3.66512807265101
[20250404_171726.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -3.665
--> '-10 < root < 0' --> substitute 0
[20250404_171726.]: Solving cubic regression for CpG#2
Coefficients: -60.0166632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#1
Root: 76.388
--> Root in between the borders! Added to results.
Coefficients: -19.33132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#10
Root: 31.437
--> Root in between the borders! Added to results.
Coefficients: -28.1616632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#2
Root: 42.956
--> Root in between the borders! Added to results.
Coefficients: -42.07832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#3
Root: 58.838
--> Root in between the borders! Added to results.
Coefficients: -2.49332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#4
Root: 4.715
--> Root in between the borders! Added to results.
Coefficients: -11.94832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#5
Root: 20.644
--> Root in between the borders! Added to results.
Coefficients: -10.36332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#6
Root: 18.159
--> Root in between the borders! Added to results.
Coefficients: -26.77132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#7
Root: 41.228
--> Root in between the borders! Added to results.
Coefficients: -70.81532996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#8
Root: 85.785
--> Root in between the borders! Added to results.
Coefficients: -1.41332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171726.]: Samplename: Sample#9
Root: 2.703
--> Root in between the borders! Added to results.
[20250404_171727.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.9349254100163
[20250404_171727.]: Samplename: Sample#1
Root: 74.935
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.6844381581493
[20250404_171727.]: Samplename: Sample#10
Root: 27.684
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.852019114379
[20250404_171727.]: Samplename: Sample#2
Root: 41.852
--> Root in between the borders! Added to results.
Hyperbolic solved: 55.8325180209418
[20250404_171727.]: Samplename: Sample#3
Root: 55.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.03519251633153
[20250404_171727.]: Samplename: Sample#4
Root: 8.035
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.1066315721853
[20250404_171727.]: Samplename: Sample#5
Root: 24.107
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2419820027673
[20250404_171727.]: Samplename: Sample#6
Root: 26.242
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0944922703422
[20250404_171727.]: Samplename: Sample#7
Root: 44.094
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.8279382585787
[20250404_171727.]: Samplename: Sample#8
Root: 85.828
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.666482392725758
[20250404_171727.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.666
--> '-10 < root < 0' --> substitute 0
[20250404_171727.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 76.3495278640236
[20250404_171727.]: Samplename: Sample#1
Root: 76.35
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.2568553570941
[20250404_171727.]: Samplename: Sample#10
Root: 28.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4089839390807
[20250404_171727.]: Samplename: Sample#2
Root: 43.409
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.5435236860146
[20250404_171727.]: Samplename: Sample#3
Root: 58.544
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.3087045690571
[20250404_171727.]: Samplename: Sample#4
Root: 10.309
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.183045165659
[20250404_171727.]: Samplename: Sample#5
Root: 22.183
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.1337769553499
[20250404_171727.]: Samplename: Sample#6
Root: 27.134
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8321096080155
[20250404_171727.]: Samplename: Sample#7
Root: 41.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6890189074743
[20250404_171727.]: Samplename: Sample#8
Root: 85.689
--> Root in between the borders! Added to results.
Hyperbolic solved: 2.42232098177269
[20250404_171727.]: Samplename: Sample#9
Root: 2.422
--> Root in between the borders! Added to results.
[20250404_171727.]: Solving cubic regression for CpG#5
Coefficients: -48.4612946127946Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -14.2119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#10
Root: 27.256
--> Root in between the borders! Added to results.
Coefficients: -25.9451041366041Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#2
Root: 44.648
--> Root in between the borders! Added to results.
Coefficients: -32.6879612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#3
Root: 53.538
--> Root in between the borders! Added to results.
Coefficients: -4.69796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#4
Root: 10.206
--> Root in between the borders! Added to results.
Coefficients: -12.0579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#5
Root: 23.695
--> Root in between the borders! Added to results.
Coefficients: -13.9179612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#6
Root: 26.778
--> Root in between the borders! Added to results.
Coefficients: -24.9119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#7
Root: 43.226
--> Root in between the borders! Added to results.
Coefficients: -63.7579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#8
Root: 88.581
--> Root in between the borders! Added to results.
Coefficients: -0.587961279461277Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171727.]: Samplename: Sample#9
Root: 1.375
--> Root in between the borders! Added to results.
[20250404_171728.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2780593622711
[20250404_171728.]: Samplename: Sample#1
Root: 79.278
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2012458984074
[20250404_171728.]: Samplename: Sample#10
Root: 30.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8474393624107
[20250404_171728.]: Samplename: Sample#2
Root: 41.847
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8423517321508
[20250404_171728.]: Samplename: Sample#3
Root: 56.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.87856046118588
[20250404_171728.]: Samplename: Sample#4
Root: 8.879
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.69015950004
[20250404_171728.]: Samplename: Sample#5
Root: 18.69
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9309263534749
[20250404_171728.]: Samplename: Sample#6
Root: 29.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8148560027697
[20250404_171728.]: Samplename: Sample#7
Root: 42.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.7501831416152
[20250404_171728.]: Samplename: Sample#8
Root: 86.75
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.51516194985267
[20250404_171728.]: Samplename: Sample#9
Root: 1.515
--> Root in between the borders! Added to results.
[20250404_171728.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 78.2565592569279
[20250404_171728.]: Samplename: Sample#1
Root: 78.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.488739349283
[20250404_171728.]: Samplename: Sample#10
Root: 25.489
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.3712258915285
[20250404_171728.]: Samplename: Sample#2
Root: 47.371
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.3142673189298
[20250404_171728.]: Samplename: Sample#3
Root: 58.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7212231360573
[20250404_171728.]: Samplename: Sample#4
Root: 11.721
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.3797485992238
[20250404_171728.]: Samplename: Sample#5
Root: 25.38
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.4095133062523
[20250404_171728.]: Samplename: Sample#6
Root: 29.41
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.5755071469546
[20250404_171728.]: Samplename: Sample#7
Root: 44.576
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.9628731021447
[20250404_171728.]: Samplename: Sample#8
Root: 85.963
--> Root in between the borders! Added to results.
Hyperbolic solved: -4.1645647175353
[20250404_171728.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.165
--> '-10 < root < 0' --> substitute 0
[20250404_171728.]: Solving cubic regression for CpG#8
Coefficients: -56.4535185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#1
Root: 72.337
--> Root in between the borders! Added to results.
Coefficients: -18.6701851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#10
Root: 28.678
--> Root in between the borders! Added to results.
Coefficients: -24.0387566137566Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#2
Root: 35.595
--> Root in between the borders! Added to results.
Coefficients: -43.9451851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#3
Root: 58.861
--> Root in between the borders! Added to results.
Coefficients: -5.70018518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#4
Root: 9.868
--> Root in between the borders! Added to results.
Coefficients: -12.4851851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#5
Root: 20.166
--> Root in between the borders! Added to results.
Coefficients: -26.8801851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#6
Root: 39.117
--> Root in between the borders! Added to results.
Coefficients: -31.8421851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#7
Root: 45.08
--> Root in between the borders! Added to results.
Coefficients: -68.0081851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#8
Root: 84.373
--> Root in between the borders! Added to results.
Coefficients: 2.07981481481482Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171728.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.026
--> '-10 < root < 0' --> substitute 0
[20250404_171728.]: Solving cubic regression for CpG#9
Coefficients: -60.8091986531987Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171728.]: Samplename: Sample#1
Root: 81.262
--> Root in between the borders! Added to results.
Coefficients: -14.5538653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171728.]: Samplename: Sample#10
Root: 24.569
--> Root in between the borders! Added to results.
Coefficients: -26.6344367484368Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171728.]: Samplename: Sample#2
Root: 45.035
--> Root in between the borders! Added to results.
Coefficients: -35.4783653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171728.]: Samplename: Sample#3
Root: 57.113
--> Root in between the borders! Added to results.
Coefficients: -4.73586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171729.]: Samplename: Sample#4
Root: 7.362
--> Root in between the borders! Added to results.
Coefficients: -12.5308653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171729.]: Samplename: Sample#5
Root: 20.907
--> Root in between the borders! Added to results.
Coefficients: -21.9358653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171729.]: Samplename: Sample#6
Root: 37.545
--> Root in between the borders! Added to results.
Coefficients: -25.1998653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171729.]: Samplename: Sample#7
Root: 42.828
--> Root in between the borders! Added to results.
Coefficients: -70.5118653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171729.]: Samplename: Sample#8
Root: 88.082
--> Root in between the borders! Added to results.
Coefficients: -0.505865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171729.]: Samplename: Sample#9
Root: 0.749
--> Root in between the borders! Added to results.
[20250404_171729.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 77.0692797356261
[20250404_171729.]: Samplename: Sample#1
Root: 77.069
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3620040447844
[20250404_171729.]: Samplename: Sample#10
Root: 28.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.5026170660315
[20250404_171729.]: Samplename: Sample#2
Root: 42.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.2972045344154
[20250404_171729.]: Samplename: Sample#3
Root: 57.297
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.82704040274281
[20250404_171729.]: Samplename: Sample#4
Root: 8.827
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8102591233667
[20250404_171729.]: Samplename: Sample#5
Root: 21.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.722865717687
[20250404_171729.]: Samplename: Sample#6
Root: 28.723
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4105098027891
[20250404_171729.]: Samplename: Sample#7
Root: 43.411
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.4143551699061
[20250404_171729.]: Samplename: Sample#8
Root: 86.414
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.237019926848022
[20250404_171729.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.237
--> '-10 < root < 0' --> substitute 0
[20250404_171729.]: Entered 'solving_equations'-Function
[20250404_171729.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_171729.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_171729.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_171729.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_171729.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_171729.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_171729.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_171729.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_171729.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_171729.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_171729.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171729.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171729.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171729.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171729.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171729.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171729.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171729.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171729.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_171729.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_171729.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_171729.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_171729.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_171729.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_171729.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_171729.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_171729.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_171729.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_171729.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_171729.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_171729.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_171729.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_171729.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_171729.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_171729.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_171729.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_171729.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_171729.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_171730.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_171730.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_171730.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171730.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171730.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171730.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171730.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171730.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171730.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171730.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171730.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_171730.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_171730.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_171730.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_171730.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_171730.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_171730.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_171730.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_171730.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_171730.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_171730.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_171730.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_171730.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_171730.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_171730.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_171730.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_171730.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_171730.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_171730.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_171730.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_171730.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_171730.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_171730.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171730.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171730.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171730.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171730.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171730.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171730.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171730.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171730.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_171730.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_171730.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171730.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171730.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171730.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171730.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171730.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171730.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171730.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171730.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_171730.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_171730.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_171730.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_171730.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_171730.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_171730.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_171730.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_171730.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_171730.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_171730.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_171730.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_171733.]: Entered 'clean_dt'-Function
[20250404_171733.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_171733.]: got experimental data
[20250404_171733.]: Entered 'clean_dt'-Function
[20250404_171733.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_171733.]: got calibration data
[20250404_171733.]: ### Starting with regression calculations ###
[20250404_171733.]: Entered 'regression_type1'-Function
[20250404_171735.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171735.]: Logging df_agg: CpG#1
[20250404_171735.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171735.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_171735.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171735.]: Entered 'hyperbolic_regression'-Function
[20250404_171735.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171736.]: Entered 'cubic_regression'-Function
[20250404_171736.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171736.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171736.]: Logging df_agg: CpG#2
[20250404_171736.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171736.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_171736.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171736.]: Entered 'hyperbolic_regression'-Function
[20250404_171736.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171737.]: Entered 'cubic_regression'-Function
[20250404_171737.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171738.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171738.]: Logging df_agg: CpG#3
[20250404_171738.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171738.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_171738.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171738.]: Entered 'hyperbolic_regression'-Function
[20250404_171738.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171740.]: Entered 'cubic_regression'-Function
[20250404_171740.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171741.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171741.]: Logging df_agg: CpG#4
[20250404_171741.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171741.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_171741.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171741.]: Entered 'hyperbolic_regression'-Function
[20250404_171741.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171743.]: Entered 'cubic_regression'-Function
[20250404_171743.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171743.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171743.]: Logging df_agg: CpG#5
[20250404_171743.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171743.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_171743.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171743.]: Entered 'hyperbolic_regression'-Function
[20250404_171743.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171745.]: Entered 'cubic_regression'-Function
[20250404_171745.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171736.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171737.]: Logging df_agg: CpG#6
[20250404_171737.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171737.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_171737.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171737.]: Entered 'hyperbolic_regression'-Function
[20250404_171737.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171738.]: Entered 'cubic_regression'-Function
[20250404_171738.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171739.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171739.]: Logging df_agg: CpG#7
[20250404_171739.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171739.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_171739.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171739.]: Entered 'hyperbolic_regression'-Function
[20250404_171739.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171740.]: Entered 'cubic_regression'-Function
[20250404_171740.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171741.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171741.]: Logging df_agg: CpG#8
[20250404_171741.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171741.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_171741.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171741.]: Entered 'hyperbolic_regression'-Function
[20250404_171741.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171742.]: Entered 'cubic_regression'-Function
[20250404_171742.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171743.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171743.]: Logging df_agg: CpG#9
[20250404_171743.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171743.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_171743.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171743.]: Entered 'hyperbolic_regression'-Function
[20250404_171743.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171744.]: Entered 'cubic_regression'-Function
[20250404_171744.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171745.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171745.]: Logging df_agg: row_means
[20250404_171745.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171745.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_171745.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171745.]: Entered 'hyperbolic_regression'-Function
[20250404_171745.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171746.]: Entered 'cubic_regression'-Function
[20250404_171746.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171754.]: Entered 'regression_type1'-Function
[20250404_171756.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171757.]: Logging df_agg: CpG#1
[20250404_171757.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171757.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_171757.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171757.]: Entered 'hyperbolic_regression'-Function
[20250404_171757.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171759.]: Entered 'cubic_regression'-Function
[20250404_171759.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171759.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171759.]: Logging df_agg: CpG#2
[20250404_171759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171759.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_171759.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171759.]: Entered 'hyperbolic_regression'-Function
[20250404_171759.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171801.]: Entered 'cubic_regression'-Function
[20250404_171801.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171801.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171802.]: Logging df_agg: CpG#3
[20250404_171802.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171802.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_171802.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171802.]: Entered 'hyperbolic_regression'-Function
[20250404_171802.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171803.]: Entered 'cubic_regression'-Function
[20250404_171803.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171804.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171804.]: Logging df_agg: CpG#4
[20250404_171804.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171804.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_171804.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171804.]: Entered 'hyperbolic_regression'-Function
[20250404_171804.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171805.]: Entered 'cubic_regression'-Function
[20250404_171805.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171806.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171806.]: Logging df_agg: CpG#5
[20250404_171806.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171806.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_171806.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171806.]: Entered 'hyperbolic_regression'-Function
[20250404_171806.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171807.]: Entered 'cubic_regression'-Function
[20250404_171807.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171757.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171758.]: Logging df_agg: CpG#6
[20250404_171758.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171758.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_171758.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171758.]: Entered 'hyperbolic_regression'-Function
[20250404_171758.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171759.]: Entered 'cubic_regression'-Function
[20250404_171759.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171800.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171800.]: Logging df_agg: CpG#7
[20250404_171800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171800.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_171800.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171800.]: Entered 'hyperbolic_regression'-Function
[20250404_171800.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171801.]: Entered 'cubic_regression'-Function
[20250404_171801.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171802.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171802.]: Logging df_agg: CpG#8
[20250404_171802.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171802.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_171802.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171802.]: Entered 'hyperbolic_regression'-Function
[20250404_171802.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171804.]: Entered 'cubic_regression'-Function
[20250404_171804.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171804.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171804.]: Logging df_agg: CpG#9
[20250404_171804.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171804.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_171804.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171804.]: Entered 'hyperbolic_regression'-Function
[20250404_171804.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171805.]: Entered 'cubic_regression'-Function
[20250404_171805.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171806.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171806.]: Logging df_agg: row_means
[20250404_171806.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171806.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_171806.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171806.]: Entered 'hyperbolic_regression'-Function
[20250404_171806.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171807.]: Entered 'cubic_regression'-Function
[20250404_171807.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171814.]: Entered 'clean_dt'-Function
[20250404_171814.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_171814.]: got experimental data
[20250404_171814.]: Entered 'clean_dt'-Function
[20250404_171814.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_171814.]: got calibration data
[20250404_171814.]: ### Starting with regression calculations ###
[20250404_171814.]: Entered 'regression_type1'-Function
[20250404_171815.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171815.]: Logging df_agg: CpG#1
[20250404_171815.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171815.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_171815.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171815.]: Entered 'hyperbolic_regression'-Function
[20250404_171815.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171817.]: Entered 'cubic_regression'-Function
[20250404_171817.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171817.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171817.]: Logging df_agg: CpG#2
[20250404_171817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171817.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_171817.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171817.]: Entered 'hyperbolic_regression'-Function
[20250404_171817.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171819.]: Entered 'cubic_regression'-Function
[20250404_171819.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171820.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171820.]: Logging df_agg: CpG#3
[20250404_171820.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171820.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_171820.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171820.]: Entered 'hyperbolic_regression'-Function
[20250404_171820.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171821.]: Entered 'cubic_regression'-Function
[20250404_171821.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171821.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171821.]: Logging df_agg: CpG#4
[20250404_171821.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171821.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_171821.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171821.]: Entered 'hyperbolic_regression'-Function
[20250404_171821.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171822.]: Entered 'cubic_regression'-Function
[20250404_171822.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171822.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171822.]: Logging df_agg: CpG#5
[20250404_171822.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171822.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_171822.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171822.]: Entered 'hyperbolic_regression'-Function
[20250404_171822.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171823.]: Entered 'cubic_regression'-Function
[20250404_171823.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171817.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171818.]: Logging df_agg: CpG#6
[20250404_171818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171818.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_171818.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171818.]: Entered 'hyperbolic_regression'-Function
[20250404_171818.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171819.]: Entered 'cubic_regression'-Function
[20250404_171819.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171819.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171819.]: Logging df_agg: CpG#7
[20250404_171819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171819.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_171819.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171819.]: Entered 'hyperbolic_regression'-Function
[20250404_171819.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171820.]: Entered 'cubic_regression'-Function
[20250404_171820.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171821.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171821.]: Logging df_agg: CpG#8
[20250404_171821.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171821.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_171821.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171821.]: Entered 'hyperbolic_regression'-Function
[20250404_171821.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171822.]: Entered 'cubic_regression'-Function
[20250404_171822.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171822.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171822.]: Logging df_agg: CpG#9
[20250404_171822.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171822.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_171822.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171822.]: Entered 'hyperbolic_regression'-Function
[20250404_171823.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171824.]: Entered 'cubic_regression'-Function
[20250404_171824.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171824.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171824.]: Logging df_agg: row_means
[20250404_171824.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171824.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_171824.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171824.]: Entered 'hyperbolic_regression'-Function
[20250404_171824.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171825.]: Entered 'cubic_regression'-Function
[20250404_171825.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171836.]: Entered 'regression_type1'-Function
[20250404_171838.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171840.]: Logging df_agg: CpG#1
[20250404_171840.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171840.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_171840.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_171840.]: Entered 'hyperbolic_regression'-Function
[20250404_171840.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171841.]: Entered 'cubic_regression'-Function
[20250404_171841.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171842.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171842.]: Logging df_agg: CpG#2
[20250404_171842.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171842.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_171842.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_171842.]: Entered 'hyperbolic_regression'-Function
[20250404_171842.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171843.]: Entered 'cubic_regression'-Function
[20250404_171843.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171844.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171844.]: Logging df_agg: CpG#3
[20250404_171844.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171844.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_171844.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_171844.]: Entered 'hyperbolic_regression'-Function
[20250404_171844.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171845.]: Entered 'cubic_regression'-Function
[20250404_171846.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171846.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171846.]: Logging df_agg: CpG#4
[20250404_171846.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171846.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_171846.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_171846.]: Entered 'hyperbolic_regression'-Function
[20250404_171846.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171848.]: Entered 'cubic_regression'-Function
[20250404_171848.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171849.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171849.]: Logging df_agg: CpG#5
[20250404_171849.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171849.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_171849.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_171849.]: Entered 'hyperbolic_regression'-Function
[20250404_171849.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171850.]: Entered 'cubic_regression'-Function
[20250404_171850.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171839.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171840.]: Logging df_agg: CpG#6
[20250404_171840.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171840.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_171840.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_171840.]: Entered 'hyperbolic_regression'-Function
[20250404_171840.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171842.]: Entered 'cubic_regression'-Function
[20250404_171842.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171842.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171842.]: Logging df_agg: CpG#7
[20250404_171842.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171842.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_171842.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_171842.]: Entered 'hyperbolic_regression'-Function
[20250404_171842.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171844.]: Entered 'cubic_regression'-Function
[20250404_171844.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171844.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171844.]: Logging df_agg: CpG#8
[20250404_171844.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171844.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_171844.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_171844.]: Entered 'hyperbolic_regression'-Function
[20250404_171844.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171845.]: Entered 'cubic_regression'-Function
[20250404_171845.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171846.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171846.]: Logging df_agg: CpG#9
[20250404_171846.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171846.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_171846.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_171846.]: Entered 'hyperbolic_regression'-Function
[20250404_171846.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171847.]: Entered 'cubic_regression'-Function
[20250404_171847.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171847.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171847.]: Logging df_agg: row_means
[20250404_171847.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171847.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_171847.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_171847.]: Entered 'hyperbolic_regression'-Function
[20250404_171847.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171849.]: Entered 'cubic_regression'-Function
[20250404_171849.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171853.]: Entered 'solving_equations'-Function
[20250404_171853.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_171853.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_171853.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_171853.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_171853.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_171853.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_171853.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_171853.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_171853.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171853.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171853.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 0
[20250404_171853.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7851657015183
[20250404_171853.]: Samplename: 12.5
Root: 10.785
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.0727152156421
[20250404_171853.]: Samplename: 25
Root: 26.073
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.2074258210424
[20250404_171854.]: Samplename: 37.5
Root: 35.207
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.9305924748583
[20250404_171854.]: Samplename: 50
Root: 47.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.2847555363015
[20250404_171854.]: Samplename: 62.5
Root: 67.285
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.735332403378
[20250404_171854.]: Samplename: 75
Root: 75.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.1313047876192
[20250404_171854.]: Samplename: 87.5
Root: 84.131
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171854.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171854.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_171854.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_171854.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_171854.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_171854.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_171854.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_171854.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_171854.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_171854.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171854.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171854.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_171854.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_171854.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_171854.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_171854.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_171855.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_171855.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_171855.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_171855.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171855.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171855.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 0
[20250404_171855.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.36665146544904
[20250404_171855.]: Samplename: 12.5
Root: 8.367
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.0855280383989
[20250404_171855.]: Samplename: 25
Root: 23.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0098400819818
[20250404_171855.]: Samplename: 37.5
Root: 37.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.0085868408378
[20250404_171855.]: Samplename: 50
Root: 51.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.7441416833696
[20250404_171855.]: Samplename: 62.5
Root: 62.744
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.6857826005162
[20250404_171855.]: Samplename: 75
Root: 76.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.3046084696663
[20250404_171855.]: Samplename: 87.5
Root: 86.305
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171855.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171855.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_171855.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_171855.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_171855.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_171855.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_171855.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_171855.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_171855.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_171855.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171855.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171855.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_171855.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_171855.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_171855.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_171855.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_171855.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_171855.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_171855.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_171855.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171855.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171855.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 0
[20250404_171855.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.27736114274885
[20250404_171855.]: Samplename: 12.5
Root: 7.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.9863834890886
[20250404_171855.]: Samplename: 25
Root: 24.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0400823094579
[20250404_171855.]: Samplename: 37.5
Root: 34.04
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3077192847199
[20250404_171855.]: Samplename: 50
Root: 52.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0861558866387
[20250404_171855.]: Samplename: 62.5
Root: 65.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.3136588178128
[20250404_171855.]: Samplename: 75
Root: 78.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.058248740059
[20250404_171855.]: Samplename: 87.5
Root: 81.058
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171855.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171855.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_171855.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_171855.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_171856.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_171856.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_171856.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_171856.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_171856.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_171856.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171856.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171856.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_171856.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_171856.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_171856.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_171856.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_171856.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_171856.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_171856.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_171856.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171856.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171856.]: ### Starting with regression calculations ###
[20250404_171856.]: Entered 'regression_type1'-Function
[20250404_171859.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_171900.]: Logging df_agg: CpG#1
[20250404_171900.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171900.]: c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_171900.]: Entered 'hyperbolic_regression'-Function
[20250404_171900.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171902.]: Entered 'cubic_regression'-Function
[20250404_171902.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171903.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_171903.]: Logging df_agg: CpG#2
[20250404_171903.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171903.]: c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_171903.]: Entered 'hyperbolic_regression'-Function
[20250404_171903.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171904.]: Entered 'cubic_regression'-Function
[20250404_171904.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171905.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_171905.]: Logging df_agg: CpG#3
[20250404_171905.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171905.]: c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_171905.]: Entered 'hyperbolic_regression'-Function
[20250404_171905.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171906.]: Entered 'cubic_regression'-Function
[20250404_171907.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171907.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_171907.]: Logging df_agg: CpG#4
[20250404_171907.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171907.]: c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_171907.]: Entered 'hyperbolic_regression'-Function
[20250404_171907.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171909.]: Entered 'cubic_regression'-Function
[20250404_171909.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171910.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_171910.]: Logging df_agg: CpG#5
[20250404_171910.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171910.]: c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_171910.]: Entered 'hyperbolic_regression'-Function
[20250404_171910.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171912.]: Entered 'cubic_regression'-Function
[20250404_171912.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171901.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_171902.]: Logging df_agg: CpG#6
[20250404_171902.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171902.]: c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_171902.]: Entered 'hyperbolic_regression'-Function
[20250404_171902.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171904.]: Entered 'cubic_regression'-Function
[20250404_171904.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171905.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_171905.]: Logging df_agg: CpG#7
[20250404_171905.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171905.]: c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_171905.]: Entered 'hyperbolic_regression'-Function
[20250404_171905.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171907.]: Entered 'cubic_regression'-Function
[20250404_171907.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171908.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_171908.]: Logging df_agg: CpG#8
[20250404_171908.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171908.]: c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_171908.]: Entered 'hyperbolic_regression'-Function
[20250404_171908.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171909.]: Entered 'cubic_regression'-Function
[20250404_171909.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171910.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_171910.]: Logging df_agg: CpG#9
[20250404_171910.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171910.]: c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_171910.]: Entered 'hyperbolic_regression'-Function
[20250404_171910.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171913.]: Entered 'cubic_regression'-Function
[20250404_171913.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171914.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_171914.]: Logging df_agg: row_means
[20250404_171914.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171914.]: c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_171914.]: Entered 'hyperbolic_regression'-Function
[20250404_171914.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171916.]: Entered 'cubic_regression'-Function
[20250404_171916.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171919.]: Entered 'solving_equations'-Function
[20250404_171919.]: Solving cubic regression for CpG#1
Coefficients: 0Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_171919.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.30533333333333Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_171919.]: Samplename: 12.5
Root: 10.279
--> Root in between the borders! Added to results.
Coefficients: -14.352Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_171919.]: Samplename: 25
Root: 21.591
--> Root in between the borders! Added to results.
Coefficients: -23.244Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_171919.]: Samplename: 37.5
Root: 36.617
--> Root in between the borders! Added to results.
Coefficients: -33.8645Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_171919.]: Samplename: 50
Root: 52.729
--> Root in between the borders! Added to results.
Coefficients: -45.318Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_171919.]: Samplename: 62.5
Root: 66.532
--> Root in between the borders! Added to results.
Coefficients: -54.857Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_171919.]: Samplename: 75
Root: 75.773
--> Root in between the borders! Added to results.
Coefficients: -62.062Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_171919.]: Samplename: 87.5
Root: 81.772
--> Root in between the borders! Added to results.
Coefficients: -90.01Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_171919.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171919.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171919.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171919.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171919.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171919.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171919.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171919.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171919.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171919.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171919.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171919.]: Solving cubic regression for CpG#3
Coefficients: 0Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_171919.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.67Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_171919.]: Samplename: 12.5
Root: 9.387
--> Root in between the borders! Added to results.
Coefficients: -14.526Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_171919.]: Samplename: 25
Root: 24.373
--> Root in between the borders! Added to results.
Coefficients: -21.71Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_171919.]: Samplename: 37.5
Root: 36.135
--> Root in between the borders! Added to results.
Coefficients: -31.8725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_171919.]: Samplename: 50
Root: 51.29
--> Root in between the borders! Added to results.
Coefficients: -42.986Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_171919.]: Samplename: 62.5
Root: 65.561
--> Root in between the borders! Added to results.
Coefficients: -54.0725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_171919.]: Samplename: 75
Root: 77.683
--> Root in between the borders! Added to results.
Coefficients: -56.7533333333333Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_171919.]: Samplename: 87.5
Root: 80.348
--> Root in between the borders! Added to results.
Coefficients: -79.762Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_171919.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171919.]: Solving cubic regression for CpG#4
Coefficients: 0Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_171919.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.65533333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_171919.]: Samplename: 12.5
Root: 11.333
--> Root in between the borders! Added to results.
Coefficients: -15.206Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_171919.]: Samplename: 25
Root: 22.933
--> Root in between the borders! Added to results.
Coefficients: -24.93Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_171919.]: Samplename: 37.5
Root: 37.542
--> Root in between the borders! Added to results.
Coefficients: -33.0395Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_171919.]: Samplename: 50
Root: 48.772
--> Root in between the borders! Added to results.
Coefficients: -49.658Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_171919.]: Samplename: 62.5
Root: 68.324
--> Root in between the borders! Added to results.
Coefficients: -55.942Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_171919.]: Samplename: 75
Root: 74.614
--> Root in between the borders! Added to results.
Coefficients: -64.9953333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_171919.]: Samplename: 87.5
Root: 82.816
--> Root in between the borders! Added to results.
Coefficients: -87.724Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_171919.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171920.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171920.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171920.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171920.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171920.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171920.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171920.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171920.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171920.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171920.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_171920.]: Solving cubic regression for CpG#6
Coefficients: 0Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_171920.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.54266666666667Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_171920.]: Samplename: 12.5
Root: 11.495
--> Root in between the borders! Added to results.
Coefficients: -15.692Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_171920.]: Samplename: 25
Root: 26.346
--> Root in between the borders! Added to results.
Coefficients: -21.804Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_171920.]: Samplename: 37.5
Root: 35.332
--> Root in between the borders! Added to results.
Coefficients: -33.2485Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_171920.]: Samplename: 50
Root: 50.228
--> Root in between the borders! Added to results.
Coefficients: -46.704Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_171920.]: Samplename: 62.5
Root: 65.055
--> Root in between the borders! Added to results.
Coefficients: -55.636Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_171920.]: Samplename: 75
Root: 73.641
--> Root in between the borders! Added to results.
Coefficients: -71.3493333333333Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_171920.]: Samplename: 87.5
Root: 86.903
--> Root in between the borders! Added to results.
Coefficients: -89.46Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_171920.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171920.]: Solving cubic regression for CpG#7
Coefficients: 0Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_171920.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.18066666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_171920.]: Samplename: 12.5
Root: 8.108
--> Root in between the borders! Added to results.
Coefficients: -10.05Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_171920.]: Samplename: 25
Root: 21.288
--> Root in between the borders! Added to results.
Coefficients: -16.236Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_171920.]: Samplename: 37.5
Root: 36.173
--> Root in between the borders! Added to results.
Coefficients: -24.8165Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_171920.]: Samplename: 50
Root: 54.247
--> Root in between the borders! Added to results.
Coefficients: -32.75Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_171920.]: Samplename: 62.5
Root: 67.087
--> Root in between the borders! Added to results.
Coefficients: -39.954Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_171920.]: Samplename: 75
Root: 76.377
--> Root in between the borders! Added to results.
Coefficients: -42.9206666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_171920.]: Samplename: 87.5
Root: 79.728
--> Root in between the borders! Added to results.
Coefficients: -66.008Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_171920.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171920.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171920.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171920.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171920.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171920.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171920.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171920.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171920.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171920.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171920.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_171920.]: Solving cubic regression for CpG#9
Coefficients: 0Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_171920.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_171920.]: Samplename: 12.5
Root: 8.93
--> Root in between the borders! Added to results.
Coefficients: -13.716Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_171921.]: Samplename: 25
Root: 24.492
--> Root in between the borders! Added to results.
Coefficients: -19.634Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_171921.]: Samplename: 37.5
Root: 35.53
--> Root in between the borders! Added to results.
Coefficients: -30.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_171921.]: Samplename: 50
Root: 52.349
--> Root in between the borders! Added to results.
Coefficients: -41.696Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_171921.]: Samplename: 62.5
Root: 65.528
--> Root in between the borders! Added to results.
Coefficients: -51.9135Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_171921.]: Samplename: 75
Root: 74.87
--> Root in between the borders! Added to results.
Coefficients: -64.5026666666667Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_171921.]: Samplename: 87.5
Root: 84.256
--> Root in between the borders! Added to results.
Coefficients: -92Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_171921.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171921.]: Solving cubic regression for row_means
Coefficients: 0Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_171921.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.70125925925926Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_171921.]: Samplename: 12.5
Root: 9.866
--> Root in between the borders! Added to results.
Coefficients: -14.126Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_171921.]: Samplename: 25
Root: 24.413
--> Root in between the borders! Added to results.
Coefficients: -21.378Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_171921.]: Samplename: 37.5
Root: 36.177
--> Root in between the borders! Added to results.
Coefficients: -31.7547777777778Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_171921.]: Samplename: 50
Root: 51.091
--> Root in between the borders! Added to results.
Coefficients: -43.9506666666667Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_171921.]: Samplename: 62.5
Root: 65.785
--> Root in between the borders! Added to results.
Coefficients: -53.632Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_171921.]: Samplename: 75
Root: 75.683
--> Root in between the borders! Added to results.
Coefficients: -61.6601481481482Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_171921.]: Samplename: 87.5
Root: 82.966
--> Root in between the borders! Added to results.
Coefficients: -83.9631111111111Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_171921.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171921.]: ### Starting with regression calculations ###
[20250404_171921.]: Entered 'regression_type1'-Function
[20250404_171923.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_171924.]: Logging df_agg: CpG#1
[20250404_171924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171924.]: c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_171924.]: Entered 'hyperbolic_regression'-Function
[20250404_171924.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171926.]: Entered 'cubic_regression'-Function
[20250404_171926.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171927.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_171927.]: Logging df_agg: CpG#2
[20250404_171927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171927.]: c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_171927.]: Entered 'hyperbolic_regression'-Function
[20250404_171927.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171929.]: Entered 'cubic_regression'-Function
[20250404_171929.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171929.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_171929.]: Logging df_agg: CpG#3
[20250404_171929.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171929.]: c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_171929.]: Entered 'hyperbolic_regression'-Function
[20250404_171929.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171931.]: Entered 'cubic_regression'-Function
[20250404_171931.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171932.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_171932.]: Logging df_agg: CpG#4
[20250404_171932.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171932.]: c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_171932.]: Entered 'hyperbolic_regression'-Function
[20250404_171932.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171934.]: Entered 'cubic_regression'-Function
[20250404_171934.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171934.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_171934.]: Logging df_agg: CpG#5
[20250404_171934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171934.]: c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_171934.]: Entered 'hyperbolic_regression'-Function
[20250404_171934.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171936.]: Entered 'cubic_regression'-Function
[20250404_171936.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171924.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_171926.]: Logging df_agg: CpG#6
[20250404_171926.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171926.]: c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_171926.]: Entered 'hyperbolic_regression'-Function
[20250404_171926.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171928.]: Entered 'cubic_regression'-Function
[20250404_171928.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171929.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_171929.]: Logging df_agg: CpG#7
[20250404_171929.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171929.]: c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_171929.]: Entered 'hyperbolic_regression'-Function
[20250404_171929.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171930.]: Entered 'cubic_regression'-Function
[20250404_171930.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171931.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_171931.]: Logging df_agg: CpG#8
[20250404_171931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171931.]: c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_171931.]: Entered 'hyperbolic_regression'-Function
[20250404_171931.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171933.]: Entered 'cubic_regression'-Function
[20250404_171933.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171933.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_171933.]: Logging df_agg: CpG#9
[20250404_171933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171933.]: c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_171933.]: Entered 'hyperbolic_regression'-Function
[20250404_171933.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171935.]: Entered 'cubic_regression'-Function
[20250404_171935.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171936.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_171936.]: Logging df_agg: row_means
[20250404_171936.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_171936.]: c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_171936.]: Entered 'hyperbolic_regression'-Function
[20250404_171936.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171938.]: Entered 'cubic_regression'-Function
[20250404_171938.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_171941.]: Entered 'solving_equations'-Function
[20250404_171941.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 78.9856894800976
[20250404_171941.]: Samplename: Sample#1
Root: 78.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 31.2695317984092
[20250404_171941.]: Samplename: Sample#10
Root: 31.27
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.7015782380441
[20250404_171941.]: Samplename: Sample#2
Root: 42.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.8152127901709
[20250404_171941.]: Samplename: Sample#3
Root: 57.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.2334360674289
[20250404_171941.]: Samplename: Sample#4
Root: 11.233
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.5293831001518
[20250404_171941.]: Samplename: Sample#5
Root: 23.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7706743072545
[20250404_171941.]: Samplename: Sample#6
Root: 24.771
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.3953425213349
[20250404_171941.]: Samplename: Sample#7
Root: 46.395
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.45071436915
[20250404_171941.]: Samplename: Sample#8
Root: 84.451
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.41337105576252
[20250404_171941.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.413
--> '-10 < root < 0' --> substitute 0
[20250404_171941.]: Solving cubic regression for CpG#2
Coefficients: -59.7333333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#1
Root: 76.346
--> Root in between the borders! Added to results.
Coefficients: -19.048Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#10
Root: 31.371
--> Root in between the borders! Added to results.
Coefficients: -27.8783333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#2
Root: 43.142
--> Root in between the borders! Added to results.
Coefficients: -41.795Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#3
Root: 59.121
--> Root in between the borders! Added to results.
Coefficients: -2.21Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#4
Root: 4.128
--> Root in between the borders! Added to results.
Coefficients: -11.665Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#5
Root: 20.292
--> Root in between the borders! Added to results.
Coefficients: -10.08Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#6
Root: 17.745
--> Root in between the borders! Added to results.
Coefficients: -26.488Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#7
Root: 41.383
--> Root in between the borders! Added to results.
Coefficients: -70.532Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#8
Root: 85.378
--> Root in between the borders! Added to results.
Coefficients: -1.13Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171942.]: Samplename: Sample#9
Root: 2.127
--> Root in between the borders! Added to results.
[20250404_171942.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.5474014641742
[20250404_171942.]: Samplename: Sample#1
Root: 74.547
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3579002775045
[20250404_171942.]: Samplename: Sample#10
Root: 28.358
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.6085496577593
[20250404_171942.]: Samplename: Sample#2
Root: 42.609
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.3286114696456
[20250404_171942.]: Samplename: Sample#3
Root: 56.329
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.99034441243248
[20250404_171942.]: Samplename: Sample#4
Root: 7.99
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7023143744962
[20250404_171942.]: Samplename: Sample#5
Root: 24.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.8868798900698
[20250404_171942.]: Samplename: Sample#6
Root: 26.887
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.8318233973603
[20250404_171942.]: Samplename: Sample#7
Root: 44.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6737871528405
[20250404_171942.]: Samplename: Sample#8
Root: 84.674
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.26200732612128
[20250404_171942.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.262
--> '-10 < root < 0' --> substitute 0
[20250404_171942.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 75.8433680333876
[20250404_171942.]: Samplename: Sample#1
Root: 75.843
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.0603248948201
[20250404_171942.]: Samplename: Sample#10
Root: 29.06
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0355928114108
[20250404_171942.]: Samplename: Sample#2
Root: 44.036
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.7751115686327
[20250404_171942.]: Samplename: Sample#3
Root: 58.775
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0319154866029
[20250404_171942.]: Samplename: Sample#4
Root: 11.032
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.9948971650737
[20250404_171942.]: Samplename: Sample#5
Root: 22.995
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.9415139419957
[20250404_171942.]: Samplename: Sample#6
Root: 27.942
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.4874049425657
[20250404_171942.]: Samplename: Sample#7
Root: 42.487
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6802730343613
[20250404_171942.]: Samplename: Sample#8
Root: 84.68
--> Root in between the borders! Added to results.
Hyperbolic solved: 3.00887785677921
[20250404_171942.]: Samplename: Sample#9
Root: 3.009
--> Root in between the borders! Added to results.
[20250404_171942.]: Solving cubic regression for CpG#5
Coefficients: -47.8373333333333Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -13.588Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#10
Root: 27.212
--> Root in between the borders! Added to results.
Coefficients: -25.3211428571429Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#2
Root: 44.85
--> Root in between the borders! Added to results.
Coefficients: -32.064Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#3
Root: 53.741
--> Root in between the borders! Added to results.
Coefficients: -4.074Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#4
Root: 9.444
--> Root in between the borders! Added to results.
Coefficients: -11.434Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#5
Root: 23.55
--> Root in between the borders! Added to results.
Coefficients: -13.294Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#6
Root: 26.722
--> Root in between the borders! Added to results.
Coefficients: -24.288Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#7
Root: 43.42
--> Root in between the borders! Added to results.
Coefficients: -63.134Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#8
Root: 88.215
--> Root in between the borders! Added to results.
Coefficients: 0.0360000000000005Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171942.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.091
--> '-10 < root < 0' --> substitute 0
[20250404_171942.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2200555510382
[20250404_171942.]: Samplename: Sample#1
Root: 79.22
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2526528381147
[20250404_171942.]: Samplename: Sample#10
Root: 30.253
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.9196854329573
[20250404_171942.]: Samplename: Sample#2
Root: 41.92
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8984354098215
[20250404_171942.]: Samplename: Sample#3
Root: 56.898
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.81576403111374
[20250404_171942.]: Samplename: Sample#4
Root: 8.816
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.6921622783918
[20250404_171942.]: Samplename: Sample#5
Root: 18.692
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9815019073132
[20250404_171942.]: Samplename: Sample#6
Root: 29.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8875178508205
[20250404_171942.]: Samplename: Sample#7
Root: 42.888
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.6303733181195
[20250404_171942.]: Samplename: Sample#8
Root: 86.63
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.38997712955107
[20250404_171942.]: Samplename: Sample#9
Root: 1.39
--> Root in between the borders! Added to results.
[20250404_171943.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 77.5278331978133
[20250404_171943.]: Samplename: Sample#1
Root: 77.528
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.0895401031897
[20250404_171943.]: Samplename: Sample#10
Root: 27.09
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.4382794903846
[20250404_171943.]: Samplename: Sample#2
Root: 48.438
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.8815971416453
[20250404_171943.]: Samplename: Sample#3
Root: 58.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.3295768294236
[20250404_171943.]: Samplename: Sample#4
Root: 13.33
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9816196357542
[20250404_171943.]: Samplename: Sample#5
Root: 26.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.9612159665911
[20250404_171943.]: Samplename: Sample#6
Root: 30.961
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.7456547820365
[20250404_171943.]: Samplename: Sample#7
Root: 45.746
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6033538318025
[20250404_171943.]: Samplename: Sample#8
Root: 84.603
--> Root in between the borders! Added to results.
Hyperbolic solved: -2.87380061592101
[20250404_171943.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.874
--> '-10 < root < 0' --> substitute 0
[20250404_171943.]: Solving cubic regression for CpG#8
Coefficients: -55.3573333333333Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#1
Root: 72.421
--> Root in between the borders! Added to results.
Coefficients: -17.574Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#10
Root: 28.533
--> Root in between the borders! Added to results.
Coefficients: -22.9425714285714Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#2
Root: 35.766
--> Root in between the borders! Added to results.
Coefficients: -42.849Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#3
Root: 59.36
--> Root in between the borders! Added to results.
Coefficients: -4.604Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#4
Root: 8.481
--> Root in between the borders! Added to results.
Coefficients: -11.389Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#5
Root: 19.519
--> Root in between the borders! Added to results.
Coefficients: -25.784Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#6
Root: 39.413
--> Root in between the borders! Added to results.
Coefficients: -30.746Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#7
Root: 45.53
--> Root in between the borders! Added to results.
Coefficients: -66.912Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#8
Root: 83.654
--> Root in between the borders! Added to results.
Coefficients: 3.176Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171943.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -6.535
--> '-10 < root < 0' --> substitute 0
[20250404_171943.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 80.5486410672961
[20250404_171943.]: Samplename: Sample#1
Root: 80.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.810468482135
[20250404_171943.]: Samplename: Sample#10
Root: 27.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.2641649294309
[20250404_171943.]: Samplename: Sample#2
Root: 46.264
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.1903653427228
[20250404_171943.]: Samplename: Sample#3
Root: 57.19
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.63886339746086
[20250404_171943.]: Samplename: Sample#4
Root: 8.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2162393845509
[20250404_171943.]: Samplename: Sample#5
Root: 24.216
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.6394430638471
[20250404_171943.]: Samplename: Sample#6
Root: 39.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.3080887012493
[20250404_171943.]: Samplename: Sample#7
Root: 44.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.3259098830063
[20250404_171943.]: Samplename: Sample#8
Root: 87.326
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.17959639730045
[20250404_171943.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.18
--> '-10 < root < 0' --> substitute 0
[20250404_171943.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 76.7568961192102
[20250404_171943.]: Samplename: Sample#1
Root: 76.757
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.8326630603664
[20250404_171943.]: Samplename: Sample#10
Root: 28.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.0145327025204
[20250404_171943.]: Samplename: Sample#2
Root: 43.015
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.6144798147902
[20250404_171943.]: Samplename: Sample#3
Root: 57.614
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.86517972238162
[20250404_171943.]: Samplename: Sample#4
Root: 8.865
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.1849817550475
[20250404_171943.]: Samplename: Sample#5
Root: 22.185
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.1973843238972
[20250404_171943.]: Samplename: Sample#6
Root: 29.197
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.9174258632975
[20250404_171943.]: Samplename: Sample#7
Root: 43.917
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6607695784409
[20250404_171943.]: Samplename: Sample#8
Root: 85.661
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.551158207550385
[20250404_171943.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.551
--> '-10 < root < 0' --> substitute 0
[20250404_171943.]: Entered 'solving_equations'-Function
[20250404_171943.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_171943.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_171943.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_171943.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_171943.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_171943.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_171943.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_171943.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_171943.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171943.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171943.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171943.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171943.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171943.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171943.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171943.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171943.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171944.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171944.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_171944.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171944.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_171944.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_171944.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_171944.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_171944.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_171944.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_171944.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_171944.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_171944.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171944.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171944.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_171944.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_171944.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_171944.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_171944.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_171944.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_171944.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_171944.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_171944.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171944.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171944.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171944.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171944.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171944.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171944.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171944.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171944.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171944.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171944.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_171944.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_171944.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_171944.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_171944.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_171944.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_171944.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_171944.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_171944.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_171944.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_171944.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171944.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171944.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_171944.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_171944.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_171944.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_171944.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_171944.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_171944.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_171944.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_171945.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171945.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171945.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171945.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171945.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171945.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171945.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171945.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171945.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171945.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171945.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_171945.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_171945.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_171945.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_171945.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_171945.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_171945.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_171945.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_171945.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_171945.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_171945.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171945.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_171945.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_171945.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_171945.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_171945.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_171945.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_171945.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_171945.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_171945.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_171945.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_171945.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[20250404_172318.]: Entered 'clean_dt'-Function
[20250404_172318.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172318.]: got experimental data
[20250404_172318.]: Entered 'clean_dt'-Function
[20250404_172318.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data)
[20250404_172318.]: got experimental data
[20250404_172320.]: Entered 'clean_dt'-Function
[20250404_172320.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172320.]: got calibration data
[20250404_172320.]: Entered 'clean_dt'-Function
[20250404_172320.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_172320.]: got calibration data
[20250404_172320.]: Entered 'hyperbolic_regression'-Function
[20250404_172320.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• On CRAN (4): 'test-algorithm_minmax_FALSE.R:80:5',
'test-algorithm_minmax_TRUE.R:76:5', 'test-hyperbolic.R:27:5',
'test-lints.R:12:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-algorithm_minmax_FALSE_re.R:170:5'): algorithm test, type 1, minmax = FALSE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_FALSE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-algorithm_minmax_TRUE_re.R:170:5'): algorithm test, type 1, minmax = TRUE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_TRUE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:17:5'): test normal function of file import of type 1 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:17:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:65:5'): test normal function of file import of type 2 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:65:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-create_aggregated.R:19:5'): test functioning of aggregated function ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-create_aggregated.R:19:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
Error: Test failures
Execution halted
Error in deferred_run(env) : could not find function "deferred_run"
Calls: <Anonymous>
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.3.4
Check: tests
Result: ERROR
Running ‘testthat.R’ [229s/315s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rBiasCorrection)
>
> local_edition(3)
>
> test_check("rBiasCorrection")
[20250404_102710.]: Entered 'clean_dt'-Function
[20250404_102710.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_102710.]: got experimental data
[20250404_102710.]: Entered 'clean_dt'-Function
[20250404_102710.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_102710.]: got calibration data
[20250404_102710.]: ### Starting with regression calculations ###
[20250404_102710.]: Entered 'regression_type1'-Function
[20250404_102711.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102711.]: Logging df_agg: CpG#1
[20250404_102711.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102711.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_102711.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102711.]: Entered 'hyperbolic_regression'-Function
[20250404_102711.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102711.]: Entered 'cubic_regression'-Function
[20250404_102711.]: 'cubic_regression': minmax = FALSE
[20250404_102711.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102711.]: Logging df_agg: CpG#2
[20250404_102711.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102711.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_102711.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102711.]: Entered 'hyperbolic_regression'-Function
[20250404_102711.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102712.]: Entered 'cubic_regression'-Function
[20250404_102712.]: 'cubic_regression': minmax = FALSE
[20250404_102712.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102712.]: Logging df_agg: CpG#3
[20250404_102712.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102712.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_102712.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102712.]: Entered 'hyperbolic_regression'-Function
[20250404_102712.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102712.]: Entered 'cubic_regression'-Function
[20250404_102712.]: 'cubic_regression': minmax = FALSE
[20250404_102712.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102712.]: Logging df_agg: CpG#4
[20250404_102713.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102713.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_102713.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102713.]: Entered 'hyperbolic_regression'-Function
[20250404_102713.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102713.]: Entered 'cubic_regression'-Function
[20250404_102713.]: 'cubic_regression': minmax = FALSE
[20250404_102713.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102713.]: Logging df_agg: CpG#5
[20250404_102713.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102713.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_102713.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102713.]: Entered 'hyperbolic_regression'-Function
[20250404_102713.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102714.]: Entered 'cubic_regression'-Function
[20250404_102714.]: 'cubic_regression': minmax = FALSE
[20250404_102712.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102712.]: Logging df_agg: CpG#6
[20250404_102712.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102712.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_102712.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102712.]: Entered 'hyperbolic_regression'-Function
[20250404_102712.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102713.]: Entered 'cubic_regression'-Function
[20250404_102713.]: 'cubic_regression': minmax = FALSE
[20250404_102713.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102713.]: Logging df_agg: CpG#7
[20250404_102713.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102713.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_102713.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102713.]: Entered 'hyperbolic_regression'-Function
[20250404_102713.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102713.]: Entered 'cubic_regression'-Function
[20250404_102713.]: 'cubic_regression': minmax = FALSE
[20250404_102713.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102713.]: Logging df_agg: CpG#8
[20250404_102713.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102713.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_102713.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102713.]: Entered 'hyperbolic_regression'-Function
[20250404_102713.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102714.]: Entered 'cubic_regression'-Function
[20250404_102714.]: 'cubic_regression': minmax = FALSE
[20250404_102714.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102714.]: Logging df_agg: CpG#9
[20250404_102714.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102714.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_102714.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102714.]: Entered 'hyperbolic_regression'-Function
[20250404_102714.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102715.]: Entered 'cubic_regression'-Function
[20250404_102715.]: 'cubic_regression': minmax = FALSE
[20250404_102715.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102715.]: Logging df_agg: row_means
[20250404_102715.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102715.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_102715.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102715.]: Entered 'hyperbolic_regression'-Function
[20250404_102715.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102716.]: Entered 'cubic_regression'-Function
[20250404_102716.]: 'cubic_regression': minmax = FALSE
[20250404_102721.]: Entered 'regression_type1'-Function
[20250404_102722.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102723.]: Logging df_agg: CpG#1
[20250404_102723.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102723.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_102723.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102723.]: Entered 'hyperbolic_regression'-Function
[20250404_102723.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102723.]: Entered 'cubic_regression'-Function
[20250404_102723.]: 'cubic_regression': minmax = FALSE
[20250404_102723.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102723.]: Logging df_agg: CpG#2
[20250404_102723.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102723.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_102723.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102723.]: Entered 'hyperbolic_regression'-Function
[20250404_102723.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102724.]: Entered 'cubic_regression'-Function
[20250404_102724.]: 'cubic_regression': minmax = FALSE
[20250404_102724.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102724.]: Logging df_agg: CpG#3
[20250404_102724.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102724.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_102724.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102724.]: Entered 'hyperbolic_regression'-Function
[20250404_102724.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102724.]: Entered 'cubic_regression'-Function
[20250404_102724.]: 'cubic_regression': minmax = FALSE
[20250404_102724.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102724.]: Logging df_agg: CpG#4
[20250404_102724.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102724.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_102724.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102724.]: Entered 'hyperbolic_regression'-Function
[20250404_102724.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102725.]: Entered 'cubic_regression'-Function
[20250404_102725.]: 'cubic_regression': minmax = FALSE
[20250404_102725.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102725.]: Logging df_agg: CpG#5
[20250404_102725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102725.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_102725.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102725.]: Entered 'hyperbolic_regression'-Function
[20250404_102725.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102726.]: Entered 'cubic_regression'-Function
[20250404_102726.]: 'cubic_regression': minmax = FALSE
[20250404_102723.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102723.]: Logging df_agg: CpG#6
[20250404_102723.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102723.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_102723.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102723.]: Entered 'hyperbolic_regression'-Function
[20250404_102723.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102724.]: Entered 'cubic_regression'-Function
[20250404_102724.]: 'cubic_regression': minmax = FALSE
[20250404_102724.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102724.]: Logging df_agg: CpG#7
[20250404_102724.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102724.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_102724.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102724.]: Entered 'hyperbolic_regression'-Function
[20250404_102724.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102725.]: Entered 'cubic_regression'-Function
[20250404_102725.]: 'cubic_regression': minmax = FALSE
[20250404_102725.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102725.]: Logging df_agg: CpG#8
[20250404_102725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102725.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_102725.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102725.]: Entered 'hyperbolic_regression'-Function
[20250404_102725.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102725.]: Entered 'cubic_regression'-Function
[20250404_102725.]: 'cubic_regression': minmax = FALSE
[20250404_102725.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102725.]: Logging df_agg: CpG#9
[20250404_102725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102725.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_102725.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102725.]: Entered 'hyperbolic_regression'-Function
[20250404_102725.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102725.]: Entered 'cubic_regression'-Function
[20250404_102725.]: 'cubic_regression': minmax = FALSE
[20250404_102726.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102726.]: Logging df_agg: row_means
[20250404_102726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102726.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_102726.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102726.]: Entered 'hyperbolic_regression'-Function
[20250404_102726.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102726.]: Entered 'cubic_regression'-Function
[20250404_102726.]: 'cubic_regression': minmax = FALSE
[20250404_102729.]: Entered 'clean_dt'-Function
[20250404_102729.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_102729.]: got experimental data
[20250404_102729.]: Entered 'clean_dt'-Function
[20250404_102729.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_102729.]: got calibration data
[20250404_102729.]: ### Starting with regression calculations ###
[20250404_102729.]: Entered 'regression_type1'-Function
[20250404_102730.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102730.]: Logging df_agg: CpG#1
[20250404_102730.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102730.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_102730.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102730.]: Entered 'hyperbolic_regression'-Function
[20250404_102730.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102731.]: Entered 'cubic_regression'-Function
[20250404_102731.]: 'cubic_regression': minmax = FALSE
[20250404_102731.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102731.]: Logging df_agg: CpG#2
[20250404_102731.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102731.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_102731.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102731.]: Entered 'hyperbolic_regression'-Function
[20250404_102731.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102731.]: Entered 'cubic_regression'-Function
[20250404_102731.]: 'cubic_regression': minmax = FALSE
[20250404_102731.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102731.]: Logging df_agg: CpG#3
[20250404_102731.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102731.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_102731.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102731.]: Entered 'hyperbolic_regression'-Function
[20250404_102731.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102732.]: Entered 'cubic_regression'-Function
[20250404_102732.]: 'cubic_regression': minmax = FALSE
[20250404_102732.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102732.]: Logging df_agg: CpG#4
[20250404_102732.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102732.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_102732.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102732.]: Entered 'hyperbolic_regression'-Function
[20250404_102732.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102732.]: Entered 'cubic_regression'-Function
[20250404_102732.]: 'cubic_regression': minmax = FALSE
[20250404_102732.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102732.]: Logging df_agg: CpG#5
[20250404_102732.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102732.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_102732.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102732.]: Entered 'hyperbolic_regression'-Function
[20250404_102732.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102733.]: Entered 'cubic_regression'-Function
[20250404_102733.]: 'cubic_regression': minmax = FALSE
[20250404_102731.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102731.]: Logging df_agg: CpG#6
[20250404_102731.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102731.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_102731.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102731.]: Entered 'hyperbolic_regression'-Function
[20250404_102731.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102731.]: Entered 'cubic_regression'-Function
[20250404_102731.]: 'cubic_regression': minmax = FALSE
[20250404_102731.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102731.]: Logging df_agg: CpG#7
[20250404_102731.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102731.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_102731.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102731.]: Entered 'hyperbolic_regression'-Function
[20250404_102731.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102732.]: Entered 'cubic_regression'-Function
[20250404_102732.]: 'cubic_regression': minmax = FALSE
[20250404_102732.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102732.]: Logging df_agg: CpG#8
[20250404_102732.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102732.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_102732.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102732.]: Entered 'hyperbolic_regression'-Function
[20250404_102732.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102732.]: Entered 'cubic_regression'-Function
[20250404_102732.]: 'cubic_regression': minmax = FALSE
[20250404_102732.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102732.]: Logging df_agg: CpG#9
[20250404_102732.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102732.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_102732.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102732.]: Entered 'hyperbolic_regression'-Function
[20250404_102732.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102733.]: Entered 'cubic_regression'-Function
[20250404_102733.]: 'cubic_regression': minmax = FALSE
[20250404_102733.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102733.]: Logging df_agg: row_means
[20250404_102733.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102733.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_102733.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102733.]: Entered 'hyperbolic_regression'-Function
[20250404_102733.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102734.]: Entered 'cubic_regression'-Function
[20250404_102734.]: 'cubic_regression': minmax = FALSE
[20250404_102738.]: Entered 'regression_type1'-Function
[20250404_102739.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102739.]: Logging df_agg: CpG#1
[20250404_102739.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102739.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_102739.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102739.]: Entered 'hyperbolic_regression'-Function
[20250404_102739.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102740.]: Entered 'cubic_regression'-Function
[20250404_102740.]: 'cubic_regression': minmax = FALSE
[20250404_102740.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102740.]: Logging df_agg: CpG#2
[20250404_102740.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102740.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_102740.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102740.]: Entered 'hyperbolic_regression'-Function
[20250404_102740.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102740.]: Entered 'cubic_regression'-Function
[20250404_102740.]: 'cubic_regression': minmax = FALSE
[20250404_102740.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102740.]: Logging df_agg: CpG#3
[20250404_102740.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102740.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_102740.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102740.]: Entered 'hyperbolic_regression'-Function
[20250404_102740.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102741.]: Entered 'cubic_regression'-Function
[20250404_102741.]: 'cubic_regression': minmax = FALSE
[20250404_102741.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102741.]: Logging df_agg: CpG#4
[20250404_102741.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102741.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_102741.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102741.]: Entered 'hyperbolic_regression'-Function
[20250404_102741.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102742.]: Entered 'cubic_regression'-Function
[20250404_102742.]: 'cubic_regression': minmax = FALSE
[20250404_102742.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102742.]: Logging df_agg: CpG#5
[20250404_102742.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102742.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_102742.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102742.]: Entered 'hyperbolic_regression'-Function
[20250404_102742.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102743.]: Entered 'cubic_regression'-Function
[20250404_102743.]: 'cubic_regression': minmax = FALSE
[20250404_102739.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102740.]: Logging df_agg: CpG#6
[20250404_102740.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102740.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_102740.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102740.]: Entered 'hyperbolic_regression'-Function
[20250404_102740.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102741.]: Entered 'cubic_regression'-Function
[20250404_102741.]: 'cubic_regression': minmax = FALSE
[20250404_102741.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102741.]: Logging df_agg: CpG#7
[20250404_102741.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102741.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_102741.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102741.]: Entered 'hyperbolic_regression'-Function
[20250404_102741.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102742.]: Entered 'cubic_regression'-Function
[20250404_102742.]: 'cubic_regression': minmax = FALSE
[20250404_102742.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102742.]: Logging df_agg: CpG#8
[20250404_102742.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102742.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_102742.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102742.]: Entered 'hyperbolic_regression'-Function
[20250404_102742.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102743.]: Entered 'cubic_regression'-Function
[20250404_102743.]: 'cubic_regression': minmax = FALSE
[20250404_102743.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102743.]: Logging df_agg: CpG#9
[20250404_102743.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102743.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_102743.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102743.]: Entered 'hyperbolic_regression'-Function
[20250404_102743.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102743.]: Entered 'cubic_regression'-Function
[20250404_102743.]: 'cubic_regression': minmax = FALSE
[20250404_102743.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102743.]: Logging df_agg: row_means
[20250404_102743.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102743.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_102743.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102743.]: Entered 'hyperbolic_regression'-Function
[20250404_102743.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102744.]: Entered 'cubic_regression'-Function
[20250404_102744.]: 'cubic_regression': minmax = FALSE
[20250404_102746.]: Entered 'solving_equations'-Function
[20250404_102746.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_102746.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_102746.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_102746.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_102746.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_102746.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_102746.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_102746.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_102746.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_102746.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_102746.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 1.13660501904968
[20250404_102746.]: Samplename: 0
Root: 1.137
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.4129696733689
[20250404_102746.]: Samplename: 12.5
Root: 11.413
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.174000526428
[20250404_102746.]: Samplename: 25
Root: 26.174
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.1050449117028
[20250404_102746.]: Samplename: 37.5
Root: 35.105
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.685500330611
[20250404_102746.]: Samplename: 50
Root: 47.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.1440494417104
[20250404_102746.]: Samplename: 62.5
Root: 67.144
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7644668894086
[20250404_102746.]: Samplename: 75
Root: 75.764
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.4054158616395
[20250404_102746.]: Samplename: 87.5
Root: 84.405
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.94827248399
[20250404_102746.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.948
--> '100 < root < 110' --> substitute 100
[20250404_102746.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_102746.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_102746.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_102746.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_102746.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_102746.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_102746.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_102746.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_102746.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_102746.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_102746.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_102746.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_102746.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_102746.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_102746.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_102746.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_102746.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_102746.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_102746.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_102746.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_102746.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 2.41558626275183
[20250404_102746.]: Samplename: 0
Root: 2.416
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.1649674907454
[20250404_102746.]: Samplename: 12.5
Root: 10.165
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.9830820412762
[20250404_102746.]: Samplename: 25
Root: 23.983
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2773619900429
[20250404_102746.]: Samplename: 37.5
Root: 37.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.8659386543864
[20250404_102746.]: Samplename: 50
Root: 50.866
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.4342273571069
[20250404_102746.]: Samplename: 62.5
Root: 62.434
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.3915260534323
[20250404_102746.]: Samplename: 75
Root: 76.392
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.159788778566
[20250404_102746.]: Samplename: 87.5
Root: 86.16
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.267759893323
[20250404_102746.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.268
--> '100 < root < 110' --> substitute 100
[20250404_102746.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_102746.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_102746.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_102746.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_102746.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_102746.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_102746.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_102746.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_102746.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_102746.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_102746.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_102746.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_102746.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_102746.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_102747.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_102747.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_102747.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_102747.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_102747.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_102747.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_102747.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 2.80068218205093
[20250404_102747.]: Samplename: 0
Root: 2.801
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.27535134596596
[20250404_102747.]: Samplename: 12.5
Root: 9.275
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4762621928197
[20250404_102747.]: Samplename: 25
Root: 25.476
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0122075735416
[20250404_102747.]: Samplename: 37.5
Root: 34.012
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.7842655662325
[20250404_102747.]: Samplename: 50
Root: 51.784
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.6732311906145
[20250404_102747.]: Samplename: 62.5
Root: 64.673
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.4326978859189
[20250404_102747.]: Samplename: 75
Root: 78.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.3427232852719
[20250404_102747.]: Samplename: 87.5
Root: 81.343
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.964406640583
[20250404_102747.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.964
--> '100 < root < 110' --> substitute 100
[20250404_102747.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: -2.13403721845678
[20250404_102747.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.134
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.5082192457956
[20250404_102747.]: Samplename: 12.5
Root: 10.508
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9164567253388
[20250404_102747.]: Samplename: 25
Root: 26.916
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.8334779159501
[20250404_102747.]: Samplename: 37.5
Root: 36.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.0097895977263
[20250404_102747.]: Samplename: 50
Root: 52.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8930527921581
[20250404_102747.]: Samplename: 62.5
Root: 64.893
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.5671055499357
[20250404_102747.]: Samplename: 75
Root: 74.567
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.5294954832669
[20250404_102747.]: Samplename: 87.5
Root: 84.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.047146466811
[20250404_102747.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.047
--> '100 < root < 110' --> substitute 100
[20250404_102747.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_102747.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_102747.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_102747.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_102747.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_102747.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_102747.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_102747.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_102747.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_102747.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_102747.]: ### Starting with regression calculations ###
[20250404_102747.]: Entered 'regression_type1'-Function
[20250404_102749.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_102749.]: Logging df_agg: CpG#1
[20250404_102749.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102749.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_102749.]: Entered 'hyperbolic_regression'-Function
[20250404_102749.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102749.]: Entered 'cubic_regression'-Function
[20250404_102749.]: 'cubic_regression': minmax = FALSE
[20250404_102749.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_102749.]: Logging df_agg: CpG#2
[20250404_102749.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102749.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_102749.]: Entered 'hyperbolic_regression'-Function
[20250404_102749.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102750.]: Entered 'cubic_regression'-Function
[20250404_102750.]: 'cubic_regression': minmax = FALSE
[20250404_102750.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_102750.]: Logging df_agg: CpG#3
[20250404_102750.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102750.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_102750.]: Entered 'hyperbolic_regression'-Function
[20250404_102750.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102751.]: Entered 'cubic_regression'-Function
[20250404_102751.]: 'cubic_regression': minmax = FALSE
[20250404_102751.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_102751.]: Logging df_agg: CpG#4
[20250404_102751.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102751.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_102751.]: Entered 'hyperbolic_regression'-Function
[20250404_102751.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102751.]: Entered 'cubic_regression'-Function
[20250404_102751.]: 'cubic_regression': minmax = FALSE
[20250404_102751.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_102751.]: Logging df_agg: CpG#5
[20250404_102751.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102751.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_102751.]: Entered 'hyperbolic_regression'-Function
[20250404_102751.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102752.]: Entered 'cubic_regression'-Function
[20250404_102752.]: 'cubic_regression': minmax = FALSE
[20250404_102750.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_102750.]: Logging df_agg: CpG#6
[20250404_102750.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102750.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_102750.]: Entered 'hyperbolic_regression'-Function
[20250404_102750.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102751.]: Entered 'cubic_regression'-Function
[20250404_102751.]: 'cubic_regression': minmax = FALSE
[20250404_102751.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_102751.]: Logging df_agg: CpG#7
[20250404_102751.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102751.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_102751.]: Entered 'hyperbolic_regression'-Function
[20250404_102751.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102751.]: Entered 'cubic_regression'-Function
[20250404_102751.]: 'cubic_regression': minmax = FALSE
[20250404_102751.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_102751.]: Logging df_agg: CpG#8
[20250404_102751.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102751.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_102752.]: Entered 'hyperbolic_regression'-Function
[20250404_102752.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102752.]: Entered 'cubic_regression'-Function
[20250404_102752.]: 'cubic_regression': minmax = FALSE
[20250404_102752.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_102752.]: Logging df_agg: CpG#9
[20250404_102752.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102752.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_102752.]: Entered 'hyperbolic_regression'-Function
[20250404_102752.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102753.]: Entered 'cubic_regression'-Function
[20250404_102753.]: 'cubic_regression': minmax = FALSE
[20250404_102753.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_102753.]: Logging df_agg: row_means
[20250404_102753.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102753.]: c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_102753.]: Entered 'hyperbolic_regression'-Function
[20250404_102753.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102753.]: Entered 'cubic_regression'-Function
[20250404_102753.]: 'cubic_regression': minmax = FALSE
[20250404_102754.]: Entered 'solving_equations'-Function
[20250404_102754.]: Solving cubic regression for CpG#1
Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_102754.]: Samplename: 0
Root: 1.334
--> Root in between the borders! Added to results.
Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_102754.]: Samplename: 12.5
Root: 11.446
--> Root in between the borders! Added to results.
Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_102754.]: Samplename: 25
Root: 22.228
--> Root in between the borders! Added to results.
Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_102754.]: Samplename: 37.5
Root: 36.374
--> Root in between the borders! Added to results.
Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_102754.]: Samplename: 50
Root: 52.044
--> Root in between the borders! Added to results.
Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_102754.]: Samplename: 62.5
Root: 66.144
--> Root in between the borders! Added to results.
Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_102754.]: Samplename: 75
Root: 75.864
--> Root in between the borders! Added to results.
Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_102754.]: Samplename: 87.5
Root: 82.254
--> Root in between the borders! Added to results.
Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_102754.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.877
--> '100 < root < 110' --> substitute 100
[20250404_102754.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102754.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102755.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102755.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102755.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102755.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102755.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102755.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102755.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102755.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_102755.]: Solving cubic regression for CpG#3
Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_102755.]: Samplename: 0
Root: 1.441
--> Root in between the borders! Added to results.
Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_102755.]: Samplename: 12.5
Root: 10.568
--> Root in between the borders! Added to results.
Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_102755.]: Samplename: 25
Root: 24.796
--> Root in between the borders! Added to results.
Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_102755.]: Samplename: 37.5
Root: 35.952
--> Root in between the borders! Added to results.
Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_102755.]: Samplename: 50
Root: 50.684
--> Root in between the borders! Added to results.
Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_102755.]: Samplename: 62.5
Root: 65.142
--> Root in between the borders! Added to results.
Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_102755.]: Samplename: 75
Root: 77.905
--> Root in between the borders! Added to results.
Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_102755.]: Samplename: 87.5
Root: 80.767
--> Root in between the borders! Added to results.
Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_102755.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.38
--> '100 < root < 110' --> substitute 100
[20250404_102755.]: Solving cubic regression for CpG#4
Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_102755.]: Samplename: 0
Root: 0.858
--> Root in between the borders! Added to results.
Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_102755.]: Samplename: 12.5
Root: 12.086
--> Root in between the borders! Added to results.
Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_102755.]: Samplename: 25
Root: 23.316
--> Root in between the borders! Added to results.
Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_102755.]: Samplename: 37.5
Root: 37.383
--> Root in between the borders! Added to results.
Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_102755.]: Samplename: 50
Root: 48.353
--> Root in between the borders! Added to results.
Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_102755.]: Samplename: 62.5
Root: 68.082
--> Root in between the borders! Added to results.
Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_102755.]: Samplename: 75
Root: 74.615
--> Root in between the borders! Added to results.
Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_102755.]: Samplename: 87.5
Root: 83.254
--> Root in between the borders! Added to results.
Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_102755.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.715
--> '100 < root < 110' --> substitute 100
[20250404_102755.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102755.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102755.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102755.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102755.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102755.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102755.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102755.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102755.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102755.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_102755.]: Solving cubic regression for CpG#6
Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_102755.]: Samplename: 0
Root: 0.349
--> Root in between the borders! Added to results.
Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_102755.]: Samplename: 12.5
Root: 11.718
--> Root in between the borders! Added to results.
Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_102755.]: Samplename: 25
Root: 26.396
--> Root in between the borders! Added to results.
Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_102755.]: Samplename: 37.5
Root: 35.301
--> Root in between the borders! Added to results.
Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_102755.]: Samplename: 50
Root: 50.134
--> Root in between the borders! Added to results.
Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_102755.]: Samplename: 62.5
Root: 64.993
--> Root in between the borders! Added to results.
Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_102755.]: Samplename: 75
Root: 73.639
--> Root in between the borders! Added to results.
Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_102755.]: Samplename: 87.5
Root: 87.043
--> Root in between the borders! Added to results.
Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_102755.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.329
--> '100 < root < 110' --> substitute 100
[20250404_102755.]: Solving cubic regression for CpG#7
Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_102755.]: Samplename: 0
Root: 2.13
--> Root in between the borders! Added to results.
Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_102755.]: Samplename: 12.5
Root: 9.973
--> Root in between the borders! Added to results.
Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_102755.]: Samplename: 25
Root: 22.206
--> Root in between the borders! Added to results.
Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_102755.]: Samplename: 37.5
Root: 35.814
--> Root in between the borders! Added to results.
Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_102755.]: Samplename: 50
Root: 53.28
--> Root in between the borders! Added to results.
Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_102755.]: Samplename: 62.5
Root: 66.598
--> Root in between the borders! Added to results.
Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_102755.]: Samplename: 75
Root: 76.575
--> Root in between the borders! Added to results.
Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_102755.]: Samplename: 87.5
Root: 80.219
--> Root in between the borders! Added to results.
Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_102755.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.506
--> '100 < root < 110' --> substitute 100
[20250404_102755.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102755.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102755.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102755.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102755.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102755.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102755.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102755.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102755.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102755.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_102755.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102755.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102755.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102755.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102755.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102755.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102755.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102755.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102755.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102755.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_102755.]: Solving cubic regression for row_means
Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_102755.]: Samplename: 0
Root: 1.287
--> Root in between the borders! Added to results.
Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_102755.]: Samplename: 12.5
Root: 10.847
--> Root in between the borders! Added to results.
Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_102755.]: Samplename: 25
Root: 24.737
--> Root in between the borders! Added to results.
Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_102755.]: Samplename: 37.5
Root: 36.02
--> Root in between the borders! Added to results.
Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_102755.]: Samplename: 50
Root: 50.639
--> Root in between the borders! Added to results.
Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_102755.]: Samplename: 62.5
Root: 65.497
--> Root in between the borders! Added to results.
Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_102755.]: Samplename: 75
Root: 75.751
--> Root in between the borders! Added to results.
Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_102755.]: Samplename: 87.5
Root: 83.403
--> Root in between the borders! Added to results.
Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_102755.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.573
--> '100 < root < 110' --> substitute 100
[20250404_102755.]: ### Starting with regression calculations ###
[20250404_102755.]: Entered 'regression_type1'-Function
[20250404_102757.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_102757.]: Logging df_agg: CpG#1
[20250404_102757.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102757.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_102757.]: Entered 'hyperbolic_regression'-Function
[20250404_102757.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102758.]: Entered 'cubic_regression'-Function
[20250404_102758.]: 'cubic_regression': minmax = FALSE
[20250404_102758.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_102758.]: Logging df_agg: CpG#2
[20250404_102758.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102758.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_102758.]: Entered 'hyperbolic_regression'-Function
[20250404_102758.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102758.]: Entered 'cubic_regression'-Function
[20250404_102758.]: 'cubic_regression': minmax = FALSE
[20250404_102758.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_102758.]: Logging df_agg: CpG#3
[20250404_102758.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102758.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_102758.]: Entered 'hyperbolic_regression'-Function
[20250404_102758.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102759.]: Entered 'cubic_regression'-Function
[20250404_102759.]: 'cubic_regression': minmax = FALSE
[20250404_102759.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_102759.]: Logging df_agg: CpG#4
[20250404_102759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102759.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_102759.]: Entered 'hyperbolic_regression'-Function
[20250404_102759.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102759.]: Entered 'cubic_regression'-Function
[20250404_102759.]: 'cubic_regression': minmax = FALSE
[20250404_102759.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_102759.]: Logging df_agg: CpG#5
[20250404_102759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102759.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_102759.]: Entered 'hyperbolic_regression'-Function
[20250404_102759.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102800.]: Entered 'cubic_regression'-Function
[20250404_102800.]: 'cubic_regression': minmax = FALSE
[20250404_102758.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_102758.]: Logging df_agg: CpG#6
[20250404_102758.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102758.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_102758.]: Entered 'hyperbolic_regression'-Function
[20250404_102758.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102759.]: Entered 'cubic_regression'-Function
[20250404_102759.]: 'cubic_regression': minmax = FALSE
[20250404_102759.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_102759.]: Logging df_agg: CpG#7
[20250404_102759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102759.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_102759.]: Entered 'hyperbolic_regression'-Function
[20250404_102759.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102800.]: Entered 'cubic_regression'-Function
[20250404_102800.]: 'cubic_regression': minmax = FALSE
[20250404_102800.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_102800.]: Logging df_agg: CpG#8
[20250404_102800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102800.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_102800.]: Entered 'hyperbolic_regression'-Function
[20250404_102800.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102801.]: Entered 'cubic_regression'-Function
[20250404_102801.]: 'cubic_regression': minmax = FALSE
[20250404_102801.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_102801.]: Logging df_agg: CpG#9
[20250404_102801.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102801.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_102801.]: Entered 'hyperbolic_regression'-Function
[20250404_102801.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102801.]: Entered 'cubic_regression'-Function
[20250404_102801.]: 'cubic_regression': minmax = FALSE
[20250404_102801.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_102801.]: Logging df_agg: row_means
[20250404_102801.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102801.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_102801.]: Entered 'hyperbolic_regression'-Function
[20250404_102801.]: 'hyperbolic_regression': minmax = FALSE
[20250404_102802.]: Entered 'cubic_regression'-Function
[20250404_102802.]: 'cubic_regression': minmax = FALSE
[20250404_102803.]: Entered 'solving_equations'-Function
[20250404_102803.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 79.8673456895745
[20250404_102803.]: Samplename: Sample#1
Root: 79.867
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.7900184340805
[20250404_102803.]: Samplename: Sample#10
Root: 29.79
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.6525415639691
[20250404_102803.]: Samplename: Sample#2
Root: 41.653
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.4652090254513
[20250404_102803.]: Samplename: Sample#3
Root: 57.465
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.2007130627765
[20250404_102803.]: Samplename: Sample#4
Root: 9.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8059600538131
[20250404_102803.]: Samplename: Sample#5
Root: 21.806
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.083796735881
[20250404_102803.]: Samplename: Sample#6
Root: 23.084
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.5034245569385
[20250404_102803.]: Samplename: Sample#7
Root: 45.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6987904075704
[20250404_102803.]: Samplename: Sample#8
Root: 85.699
--> Root in between the borders! Added to results.
Hyperbolic solved: -3.66512807265101
[20250404_102803.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -3.665
--> '-10 < root < 0' --> substitute 0
[20250404_102803.]: Solving cubic regression for CpG#2
Coefficients: -60.0166632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#1
Root: 76.388
--> Root in between the borders! Added to results.
Coefficients: -19.33132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#10
Root: 31.437
--> Root in between the borders! Added to results.
Coefficients: -28.1616632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#2
Root: 42.956
--> Root in between the borders! Added to results.
Coefficients: -42.07832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#3
Root: 58.838
--> Root in between the borders! Added to results.
Coefficients: -2.49332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#4
Root: 4.715
--> Root in between the borders! Added to results.
Coefficients: -11.94832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#5
Root: 20.644
--> Root in between the borders! Added to results.
Coefficients: -10.36332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#6
Root: 18.159
--> Root in between the borders! Added to results.
Coefficients: -26.77132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#7
Root: 41.228
--> Root in between the borders! Added to results.
Coefficients: -70.81532996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#8
Root: 85.785
--> Root in between the borders! Added to results.
Coefficients: -1.41332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102803.]: Samplename: Sample#9
Root: 2.703
--> Root in between the borders! Added to results.
[20250404_102803.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.9349254100163
[20250404_102803.]: Samplename: Sample#1
Root: 74.935
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.6844381581493
[20250404_102803.]: Samplename: Sample#10
Root: 27.684
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.852019114379
[20250404_102803.]: Samplename: Sample#2
Root: 41.852
--> Root in between the borders! Added to results.
Hyperbolic solved: 55.8325180209418
[20250404_102803.]: Samplename: Sample#3
Root: 55.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.03519251633153
[20250404_102803.]: Samplename: Sample#4
Root: 8.035
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.1066315721853
[20250404_102803.]: Samplename: Sample#5
Root: 24.107
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2419820027673
[20250404_102803.]: Samplename: Sample#6
Root: 26.242
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0944922703422
[20250404_102803.]: Samplename: Sample#7
Root: 44.094
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.8279382585787
[20250404_102803.]: Samplename: Sample#8
Root: 85.828
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.666482392725758
[20250404_102803.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.666
--> '-10 < root < 0' --> substitute 0
[20250404_102803.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 76.3495278640236
[20250404_102803.]: Samplename: Sample#1
Root: 76.35
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.2568553570941
[20250404_102803.]: Samplename: Sample#10
Root: 28.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4089839390807
[20250404_102803.]: Samplename: Sample#2
Root: 43.409
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.5435236860146
[20250404_102803.]: Samplename: Sample#3
Root: 58.544
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.3087045690571
[20250404_102803.]: Samplename: Sample#4
Root: 10.309
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.183045165659
[20250404_102803.]: Samplename: Sample#5
Root: 22.183
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.1337769553499
[20250404_102803.]: Samplename: Sample#6
Root: 27.134
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8321096080155
[20250404_102803.]: Samplename: Sample#7
Root: 41.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6890189074743
[20250404_102803.]: Samplename: Sample#8
Root: 85.689
--> Root in between the borders! Added to results.
Hyperbolic solved: 2.42232098177269
[20250404_102803.]: Samplename: Sample#9
Root: 2.422
--> Root in between the borders! Added to results.
[20250404_102803.]: Solving cubic regression for CpG#5
Coefficients: -48.4612946127946Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -14.2119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#10
Root: 27.256
--> Root in between the borders! Added to results.
Coefficients: -25.9451041366041Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#2
Root: 44.648
--> Root in between the borders! Added to results.
Coefficients: -32.6879612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#3
Root: 53.538
--> Root in between the borders! Added to results.
Coefficients: -4.69796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#4
Root: 10.206
--> Root in between the borders! Added to results.
Coefficients: -12.0579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#5
Root: 23.695
--> Root in between the borders! Added to results.
Coefficients: -13.9179612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#6
Root: 26.778
--> Root in between the borders! Added to results.
Coefficients: -24.9119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#7
Root: 43.226
--> Root in between the borders! Added to results.
Coefficients: -63.7579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#8
Root: 88.581
--> Root in between the borders! Added to results.
Coefficients: -0.587961279461277Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102803.]: Samplename: Sample#9
Root: 1.375
--> Root in between the borders! Added to results.
[20250404_102804.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2780593622711
[20250404_102804.]: Samplename: Sample#1
Root: 79.278
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2012458984074
[20250404_102804.]: Samplename: Sample#10
Root: 30.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8474393624107
[20250404_102804.]: Samplename: Sample#2
Root: 41.847
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8423517321508
[20250404_102804.]: Samplename: Sample#3
Root: 56.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.87856046118588
[20250404_102804.]: Samplename: Sample#4
Root: 8.879
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.69015950004
[20250404_102804.]: Samplename: Sample#5
Root: 18.69
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9309263534749
[20250404_102804.]: Samplename: Sample#6
Root: 29.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8148560027697
[20250404_102804.]: Samplename: Sample#7
Root: 42.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.7501831416152
[20250404_102804.]: Samplename: Sample#8
Root: 86.75
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.51516194985267
[20250404_102804.]: Samplename: Sample#9
Root: 1.515
--> Root in between the borders! Added to results.
[20250404_102804.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 78.2565592569279
[20250404_102804.]: Samplename: Sample#1
Root: 78.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.488739349283
[20250404_102804.]: Samplename: Sample#10
Root: 25.489
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.3712258915285
[20250404_102804.]: Samplename: Sample#2
Root: 47.371
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.3142673189298
[20250404_102804.]: Samplename: Sample#3
Root: 58.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7212231360573
[20250404_102804.]: Samplename: Sample#4
Root: 11.721
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.3797485992238
[20250404_102804.]: Samplename: Sample#5
Root: 25.38
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.4095133062523
[20250404_102804.]: Samplename: Sample#6
Root: 29.41
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.5755071469546
[20250404_102804.]: Samplename: Sample#7
Root: 44.576
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.9628731021447
[20250404_102804.]: Samplename: Sample#8
Root: 85.963
--> Root in between the borders! Added to results.
Hyperbolic solved: -4.1645647175353
[20250404_102804.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.165
--> '-10 < root < 0' --> substitute 0
[20250404_102804.]: Solving cubic regression for CpG#8
Coefficients: -56.4535185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#1
Root: 72.337
--> Root in between the borders! Added to results.
Coefficients: -18.6701851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#10
Root: 28.678
--> Root in between the borders! Added to results.
Coefficients: -24.0387566137566Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#2
Root: 35.595
--> Root in between the borders! Added to results.
Coefficients: -43.9451851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#3
Root: 58.861
--> Root in between the borders! Added to results.
Coefficients: -5.70018518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#4
Root: 9.868
--> Root in between the borders! Added to results.
Coefficients: -12.4851851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#5
Root: 20.166
--> Root in between the borders! Added to results.
Coefficients: -26.8801851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#6
Root: 39.117
--> Root in between the borders! Added to results.
Coefficients: -31.8421851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#7
Root: 45.08
--> Root in between the borders! Added to results.
Coefficients: -68.0081851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#8
Root: 84.373
--> Root in between the borders! Added to results.
Coefficients: 2.07981481481482Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.026
--> '-10 < root < 0' --> substitute 0
[20250404_102804.]: Solving cubic regression for CpG#9
Coefficients: -60.8091986531987Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#1
Root: 81.262
--> Root in between the borders! Added to results.
Coefficients: -14.5538653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#10
Root: 24.569
--> Root in between the borders! Added to results.
Coefficients: -26.6344367484368Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#2
Root: 45.035
--> Root in between the borders! Added to results.
Coefficients: -35.4783653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#3
Root: 57.113
--> Root in between the borders! Added to results.
Coefficients: -4.73586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#4
Root: 7.362
--> Root in between the borders! Added to results.
Coefficients: -12.5308653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#5
Root: 20.907
--> Root in between the borders! Added to results.
Coefficients: -21.9358653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#6
Root: 37.545
--> Root in between the borders! Added to results.
Coefficients: -25.1998653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#7
Root: 42.828
--> Root in between the borders! Added to results.
Coefficients: -70.5118653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#8
Root: 88.082
--> Root in between the borders! Added to results.
Coefficients: -0.505865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: Sample#9
Root: 0.749
--> Root in between the borders! Added to results.
[20250404_102804.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 77.0692797356261
[20250404_102804.]: Samplename: Sample#1
Root: 77.069
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3620040447844
[20250404_102804.]: Samplename: Sample#10
Root: 28.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.5026170660315
[20250404_102804.]: Samplename: Sample#2
Root: 42.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.2972045344154
[20250404_102804.]: Samplename: Sample#3
Root: 57.297
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.82704040274281
[20250404_102804.]: Samplename: Sample#4
Root: 8.827
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8102591233667
[20250404_102804.]: Samplename: Sample#5
Root: 21.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.722865717687
[20250404_102804.]: Samplename: Sample#6
Root: 28.723
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4105098027891
[20250404_102804.]: Samplename: Sample#7
Root: 43.411
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.4143551699061
[20250404_102804.]: Samplename: Sample#8
Root: 86.414
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.237019926848022
[20250404_102804.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.237
--> '-10 < root < 0' --> substitute 0
[20250404_102804.]: Entered 'solving_equations'-Function
[20250404_102804.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_102804.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_102804.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_102804.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_102804.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_102804.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_102804.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_102804.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_102804.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_102804.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_102804.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102804.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102804.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102804.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102804.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102804.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102804.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102804.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102804.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_102804.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_102804.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_102804.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_102804.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_102804.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_102804.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_102804.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_102804.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_102804.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_102804.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_102804.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_102804.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_102804.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_102804.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_102804.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_102804.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_102804.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_102804.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_102804.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_102804.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_102804.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_102804.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102804.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102804.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102804.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102804.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102804.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102804.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102804.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102804.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_102804.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_102804.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_102804.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_102804.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_102804.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_102804.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_102804.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_102804.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_102804.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_102804.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_102804.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_102804.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_102804.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_102804.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_102804.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_102804.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_102804.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_102804.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_102804.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_102804.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_102804.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_102804.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_102804.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_102804.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102804.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_102805.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_102805.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_102805.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_102805.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_102805.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_102805.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_102805.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_102805.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_102805.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_102805.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_102805.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_102806.]: Entered 'clean_dt'-Function
[20250404_102806.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_102806.]: got experimental data
[20250404_102806.]: Entered 'clean_dt'-Function
[20250404_102806.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_102806.]: got calibration data
[20250404_102807.]: ### Starting with regression calculations ###
[20250404_102807.]: Entered 'regression_type1'-Function
[20250404_102807.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102807.]: Logging df_agg: CpG#1
[20250404_102807.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102807.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_102807.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102807.]: Entered 'hyperbolic_regression'-Function
[20250404_102807.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102808.]: Entered 'cubic_regression'-Function
[20250404_102808.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102808.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102808.]: Logging df_agg: CpG#2
[20250404_102808.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102808.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_102808.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102808.]: Entered 'hyperbolic_regression'-Function
[20250404_102808.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102809.]: Entered 'cubic_regression'-Function
[20250404_102809.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102810.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102810.]: Logging df_agg: CpG#3
[20250404_102810.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102810.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_102810.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102810.]: Entered 'hyperbolic_regression'-Function
[20250404_102810.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102810.]: Entered 'cubic_regression'-Function
[20250404_102810.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102810.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102810.]: Logging df_agg: CpG#4
[20250404_102810.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102810.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_102810.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102810.]: Entered 'hyperbolic_regression'-Function
[20250404_102810.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102811.]: Entered 'cubic_regression'-Function
[20250404_102811.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102811.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102811.]: Logging df_agg: CpG#5
[20250404_102811.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102811.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_102811.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102811.]: Entered 'hyperbolic_regression'-Function
[20250404_102811.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102812.]: Entered 'cubic_regression'-Function
[20250404_102812.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102808.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102808.]: Logging df_agg: CpG#6
[20250404_102808.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102808.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_102808.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102808.]: Entered 'hyperbolic_regression'-Function
[20250404_102808.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102809.]: Entered 'cubic_regression'-Function
[20250404_102809.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102809.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102809.]: Logging df_agg: CpG#7
[20250404_102809.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102809.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_102809.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102809.]: Entered 'hyperbolic_regression'-Function
[20250404_102809.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102810.]: Entered 'cubic_regression'-Function
[20250404_102810.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102810.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102810.]: Logging df_agg: CpG#8
[20250404_102810.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102810.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_102810.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102810.]: Entered 'hyperbolic_regression'-Function
[20250404_102810.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102811.]: Entered 'cubic_regression'-Function
[20250404_102811.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102811.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102811.]: Logging df_agg: CpG#9
[20250404_102811.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102811.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_102811.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102811.]: Entered 'hyperbolic_regression'-Function
[20250404_102812.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102812.]: Entered 'cubic_regression'-Function
[20250404_102812.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102812.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102812.]: Logging df_agg: row_means
[20250404_102812.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102812.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_102812.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102812.]: Entered 'hyperbolic_regression'-Function
[20250404_102812.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102813.]: Entered 'cubic_regression'-Function
[20250404_102813.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102817.]: Entered 'regression_type1'-Function
[20250404_102818.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102819.]: Logging df_agg: CpG#1
[20250404_102819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102819.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_102819.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102819.]: Entered 'hyperbolic_regression'-Function
[20250404_102819.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102819.]: Entered 'cubic_regression'-Function
[20250404_102819.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102819.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102820.]: Logging df_agg: CpG#2
[20250404_102820.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102820.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_102820.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102820.]: Entered 'hyperbolic_regression'-Function
[20250404_102820.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102820.]: Entered 'cubic_regression'-Function
[20250404_102820.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102820.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102820.]: Logging df_agg: CpG#3
[20250404_102820.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102820.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_102820.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102820.]: Entered 'hyperbolic_regression'-Function
[20250404_102820.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102821.]: Entered 'cubic_regression'-Function
[20250404_102821.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102821.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102821.]: Logging df_agg: CpG#4
[20250404_102821.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102821.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_102821.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102821.]: Entered 'hyperbolic_regression'-Function
[20250404_102821.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102822.]: Entered 'cubic_regression'-Function
[20250404_102822.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102822.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102822.]: Logging df_agg: CpG#5
[20250404_102822.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102822.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_102822.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102822.]: Entered 'hyperbolic_regression'-Function
[20250404_102822.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102823.]: Entered 'cubic_regression'-Function
[20250404_102823.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102819.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102819.]: Logging df_agg: CpG#6
[20250404_102819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102819.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_102819.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102819.]: Entered 'hyperbolic_regression'-Function
[20250404_102819.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102820.]: Entered 'cubic_regression'-Function
[20250404_102820.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102820.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102820.]: Logging df_agg: CpG#7
[20250404_102820.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102820.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_102820.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102820.]: Entered 'hyperbolic_regression'-Function
[20250404_102820.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102821.]: Entered 'cubic_regression'-Function
[20250404_102821.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102821.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102821.]: Logging df_agg: CpG#8
[20250404_102821.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102821.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_102821.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102821.]: Entered 'hyperbolic_regression'-Function
[20250404_102821.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102822.]: Entered 'cubic_regression'-Function
[20250404_102822.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102822.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102822.]: Logging df_agg: CpG#9
[20250404_102822.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102822.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_102822.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102822.]: Entered 'hyperbolic_regression'-Function
[20250404_102822.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102823.]: Entered 'cubic_regression'-Function
[20250404_102823.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102823.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102823.]: Logging df_agg: row_means
[20250404_102823.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102823.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_102823.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102823.]: Entered 'hyperbolic_regression'-Function
[20250404_102823.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102824.]: Entered 'cubic_regression'-Function
[20250404_102824.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102827.]: Entered 'clean_dt'-Function
[20250404_102827.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_102827.]: got experimental data
[20250404_102827.]: Entered 'clean_dt'-Function
[20250404_102827.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_102827.]: got calibration data
[20250404_102827.]: ### Starting with regression calculations ###
[20250404_102827.]: Entered 'regression_type1'-Function
[20250404_102828.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102828.]: Logging df_agg: CpG#1
[20250404_102828.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102828.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_102828.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102828.]: Entered 'hyperbolic_regression'-Function
[20250404_102828.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102828.]: Entered 'cubic_regression'-Function
[20250404_102828.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102829.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102829.]: Logging df_agg: CpG#2
[20250404_102829.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102829.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_102829.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102829.]: Entered 'hyperbolic_regression'-Function
[20250404_102829.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102829.]: Entered 'cubic_regression'-Function
[20250404_102829.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102830.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102830.]: Logging df_agg: CpG#3
[20250404_102830.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102830.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_102830.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102830.]: Entered 'hyperbolic_regression'-Function
[20250404_102830.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102830.]: Entered 'cubic_regression'-Function
[20250404_102830.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102830.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102830.]: Logging df_agg: CpG#4
[20250404_102830.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102830.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_102830.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102830.]: Entered 'hyperbolic_regression'-Function
[20250404_102830.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102831.]: Entered 'cubic_regression'-Function
[20250404_102831.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102831.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102831.]: Logging df_agg: CpG#5
[20250404_102831.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102831.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_102831.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102831.]: Entered 'hyperbolic_regression'-Function
[20250404_102832.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102832.]: Entered 'cubic_regression'-Function
[20250404_102832.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102829.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102829.]: Logging df_agg: CpG#6
[20250404_102829.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102829.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_102829.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102829.]: Entered 'hyperbolic_regression'-Function
[20250404_102829.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102830.]: Entered 'cubic_regression'-Function
[20250404_102830.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102830.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102830.]: Logging df_agg: CpG#7
[20250404_102830.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102830.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_102830.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102830.]: Entered 'hyperbolic_regression'-Function
[20250404_102830.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102830.]: Entered 'cubic_regression'-Function
[20250404_102830.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102831.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102831.]: Logging df_agg: CpG#8
[20250404_102831.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102831.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_102831.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102831.]: Entered 'hyperbolic_regression'-Function
[20250404_102831.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102831.]: Entered 'cubic_regression'-Function
[20250404_102831.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102832.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102832.]: Logging df_agg: CpG#9
[20250404_102832.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102832.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_102832.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102832.]: Entered 'hyperbolic_regression'-Function
[20250404_102832.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102832.]: Entered 'cubic_regression'-Function
[20250404_102832.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102833.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102833.]: Logging df_agg: row_means
[20250404_102833.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102833.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_102833.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102833.]: Entered 'hyperbolic_regression'-Function
[20250404_102833.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102833.]: Entered 'cubic_regression'-Function
[20250404_102833.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102838.]: Entered 'regression_type1'-Function
[20250404_102839.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102839.]: Logging df_agg: CpG#1
[20250404_102839.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102839.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_102839.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_102839.]: Entered 'hyperbolic_regression'-Function
[20250404_102839.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102840.]: Entered 'cubic_regression'-Function
[20250404_102840.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102840.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102840.]: Logging df_agg: CpG#2
[20250404_102840.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102840.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_102840.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_102840.]: Entered 'hyperbolic_regression'-Function
[20250404_102840.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102841.]: Entered 'cubic_regression'-Function
[20250404_102841.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102842.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102842.]: Logging df_agg: CpG#3
[20250404_102842.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102842.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_102842.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_102842.]: Entered 'hyperbolic_regression'-Function
[20250404_102842.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102842.]: Entered 'cubic_regression'-Function
[20250404_102842.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102843.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102843.]: Logging df_agg: CpG#4
[20250404_102843.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102843.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_102843.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_102843.]: Entered 'hyperbolic_regression'-Function
[20250404_102843.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102843.]: Entered 'cubic_regression'-Function
[20250404_102843.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102843.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102843.]: Logging df_agg: CpG#5
[20250404_102843.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102843.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_102843.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_102843.]: Entered 'hyperbolic_regression'-Function
[20250404_102843.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102844.]: Entered 'cubic_regression'-Function
[20250404_102844.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102839.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102840.]: Logging df_agg: CpG#6
[20250404_102840.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102840.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_102840.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_102840.]: Entered 'hyperbolic_regression'-Function
[20250404_102840.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102840.]: Entered 'cubic_regression'-Function
[20250404_102840.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102841.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102841.]: Logging df_agg: CpG#7
[20250404_102841.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102841.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_102841.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_102841.]: Entered 'hyperbolic_regression'-Function
[20250404_102841.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102841.]: Entered 'cubic_regression'-Function
[20250404_102841.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102842.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102842.]: Logging df_agg: CpG#8
[20250404_102842.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102842.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_102842.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_102842.]: Entered 'hyperbolic_regression'-Function
[20250404_102842.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102842.]: Entered 'cubic_regression'-Function
[20250404_102842.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102843.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102843.]: Logging df_agg: CpG#9
[20250404_102843.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102843.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_102843.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_102843.]: Entered 'hyperbolic_regression'-Function
[20250404_102843.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102843.]: Entered 'cubic_regression'-Function
[20250404_102843.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102843.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102843.]: Logging df_agg: row_means
[20250404_102843.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102843.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_102843.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_102843.]: Entered 'hyperbolic_regression'-Function
[20250404_102843.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102844.]: Entered 'cubic_regression'-Function
[20250404_102844.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102846.]: Entered 'solving_equations'-Function
[20250404_102846.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_102846.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_102846.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_102846.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_102846.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_102846.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_102846.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_102846.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_102846.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102846.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102846.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 0
[20250404_102846.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7851657015183
[20250404_102846.]: Samplename: 12.5
Root: 10.785
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.0727152156421
[20250404_102846.]: Samplename: 25
Root: 26.073
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.2074258210424
[20250404_102846.]: Samplename: 37.5
Root: 35.207
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.9305924748583
[20250404_102846.]: Samplename: 50
Root: 47.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.2847555363015
[20250404_102846.]: Samplename: 62.5
Root: 67.285
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.735332403378
[20250404_102846.]: Samplename: 75
Root: 75.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.1313047876192
[20250404_102846.]: Samplename: 87.5
Root: 84.131
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102846.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102846.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_102846.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_102846.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_102846.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_102846.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_102846.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_102846.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_102846.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_102847.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102847.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102847.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_102847.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_102847.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_102847.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_102847.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_102847.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_102847.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_102847.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_102847.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102847.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102847.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 0
[20250404_102847.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.36665146544904
[20250404_102847.]: Samplename: 12.5
Root: 8.367
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.0855280383989
[20250404_102847.]: Samplename: 25
Root: 23.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0098400819818
[20250404_102847.]: Samplename: 37.5
Root: 37.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.0085868408378
[20250404_102847.]: Samplename: 50
Root: 51.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.7441416833696
[20250404_102847.]: Samplename: 62.5
Root: 62.744
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.6857826005162
[20250404_102847.]: Samplename: 75
Root: 76.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.3046084696663
[20250404_102847.]: Samplename: 87.5
Root: 86.305
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102847.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102847.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_102847.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_102847.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_102847.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_102847.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_102847.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_102847.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_102847.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_102847.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102847.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102847.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_102847.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_102847.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_102847.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_102847.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_102847.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_102847.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_102847.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_102847.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102847.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102847.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 0
[20250404_102847.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.27736114274885
[20250404_102847.]: Samplename: 12.5
Root: 7.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.9863834890886
[20250404_102847.]: Samplename: 25
Root: 24.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0400823094579
[20250404_102847.]: Samplename: 37.5
Root: 34.04
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3077192847199
[20250404_102847.]: Samplename: 50
Root: 52.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0861558866387
[20250404_102847.]: Samplename: 62.5
Root: 65.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.3136588178128
[20250404_102847.]: Samplename: 75
Root: 78.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.058248740059
[20250404_102847.]: Samplename: 87.5
Root: 81.058
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102847.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102847.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_102847.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_102847.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_102847.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_102847.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_102847.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_102847.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_102847.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_102847.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102847.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102847.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_102847.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_102847.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_102847.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_102847.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_102847.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_102847.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_102847.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_102847.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102847.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102847.]: ### Starting with regression calculations ###
[20250404_102847.]: Entered 'regression_type1'-Function
[20250404_102849.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_102849.]: Logging df_agg: CpG#1
[20250404_102849.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102849.]: c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_102849.]: Entered 'hyperbolic_regression'-Function
[20250404_102849.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102850.]: Entered 'cubic_regression'-Function
[20250404_102850.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102850.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_102850.]: Logging df_agg: CpG#2
[20250404_102850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102850.]: c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_102850.]: Entered 'hyperbolic_regression'-Function
[20250404_102850.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102851.]: Entered 'cubic_regression'-Function
[20250404_102851.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102851.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_102851.]: Logging df_agg: CpG#3
[20250404_102851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102851.]: c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_102851.]: Entered 'hyperbolic_regression'-Function
[20250404_102851.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102852.]: Entered 'cubic_regression'-Function
[20250404_102852.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102852.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_102852.]: Logging df_agg: CpG#4
[20250404_102852.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102852.]: c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_102852.]: Entered 'hyperbolic_regression'-Function
[20250404_102852.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102853.]: Entered 'cubic_regression'-Function
[20250404_102853.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102853.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_102853.]: Logging df_agg: CpG#5
[20250404_102853.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102853.]: c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_102853.]: Entered 'hyperbolic_regression'-Function
[20250404_102853.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102853.]: Entered 'cubic_regression'-Function
[20250404_102853.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102850.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_102850.]: Logging df_agg: CpG#6
[20250404_102850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102850.]: c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_102850.]: Entered 'hyperbolic_regression'-Function
[20250404_102850.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102851.]: Entered 'cubic_regression'-Function
[20250404_102851.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102851.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_102851.]: Logging df_agg: CpG#7
[20250404_102851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102851.]: c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_102851.]: Entered 'hyperbolic_regression'-Function
[20250404_102851.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102852.]: Entered 'cubic_regression'-Function
[20250404_102852.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102852.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_102852.]: Logging df_agg: CpG#8
[20250404_102852.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102852.]: c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_102852.]: Entered 'hyperbolic_regression'-Function
[20250404_102852.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102853.]: Entered 'cubic_regression'-Function
[20250404_102853.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102853.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_102853.]: Logging df_agg: CpG#9
[20250404_102853.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102853.]: c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_102853.]: Entered 'hyperbolic_regression'-Function
[20250404_102853.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102854.]: Entered 'cubic_regression'-Function
[20250404_102854.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102854.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_102854.]: Logging df_agg: row_means
[20250404_102854.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102854.]: c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_102854.]: Entered 'hyperbolic_regression'-Function
[20250404_102854.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102854.]: Entered 'cubic_regression'-Function
[20250404_102854.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102856.]: Entered 'solving_equations'-Function
[20250404_102856.]: Solving cubic regression for CpG#1
Coefficients: 0Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_102856.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.30533333333333Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_102856.]: Samplename: 12.5
Root: 10.279
--> Root in between the borders! Added to results.
Coefficients: -14.352Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_102856.]: Samplename: 25
Root: 21.591
--> Root in between the borders! Added to results.
Coefficients: -23.244Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_102856.]: Samplename: 37.5
Root: 36.617
--> Root in between the borders! Added to results.
Coefficients: -33.8645Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_102856.]: Samplename: 50
Root: 52.729
--> Root in between the borders! Added to results.
Coefficients: -45.318Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_102856.]: Samplename: 62.5
Root: 66.532
--> Root in between the borders! Added to results.
Coefficients: -54.857Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_102856.]: Samplename: 75
Root: 75.773
--> Root in between the borders! Added to results.
Coefficients: -62.062Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_102856.]: Samplename: 87.5
Root: 81.772
--> Root in between the borders! Added to results.
Coefficients: -90.01Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_102856.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102856.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102856.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102856.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102856.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102856.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102856.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102856.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102856.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102856.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102856.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102856.]: Solving cubic regression for CpG#3
Coefficients: 0Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_102856.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.67Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_102856.]: Samplename: 12.5
Root: 9.387
--> Root in between the borders! Added to results.
Coefficients: -14.526Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_102856.]: Samplename: 25
Root: 24.373
--> Root in between the borders! Added to results.
Coefficients: -21.71Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_102856.]: Samplename: 37.5
Root: 36.135
--> Root in between the borders! Added to results.
Coefficients: -31.8725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_102856.]: Samplename: 50
Root: 51.29
--> Root in between the borders! Added to results.
Coefficients: -42.986Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_102856.]: Samplename: 62.5
Root: 65.561
--> Root in between the borders! Added to results.
Coefficients: -54.0725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_102856.]: Samplename: 75
Root: 77.683
--> Root in between the borders! Added to results.
Coefficients: -56.7533333333333Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_102856.]: Samplename: 87.5
Root: 80.348
--> Root in between the borders! Added to results.
Coefficients: -79.762Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_102856.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102856.]: Solving cubic regression for CpG#4
Coefficients: 0Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_102856.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.65533333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_102856.]: Samplename: 12.5
Root: 11.333
--> Root in between the borders! Added to results.
Coefficients: -15.206Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_102856.]: Samplename: 25
Root: 22.933
--> Root in between the borders! Added to results.
Coefficients: -24.93Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_102856.]: Samplename: 37.5
Root: 37.542
--> Root in between the borders! Added to results.
Coefficients: -33.0395Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_102856.]: Samplename: 50
Root: 48.772
--> Root in between the borders! Added to results.
Coefficients: -49.658Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_102856.]: Samplename: 62.5
Root: 68.324
--> Root in between the borders! Added to results.
Coefficients: -55.942Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_102856.]: Samplename: 75
Root: 74.614
--> Root in between the borders! Added to results.
Coefficients: -64.9953333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_102856.]: Samplename: 87.5
Root: 82.816
--> Root in between the borders! Added to results.
Coefficients: -87.724Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_102856.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102856.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102856.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102856.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102856.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102856.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102856.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102856.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102856.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102856.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102856.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_102857.]: Solving cubic regression for CpG#6
Coefficients: 0Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_102857.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.54266666666667Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_102857.]: Samplename: 12.5
Root: 11.495
--> Root in between the borders! Added to results.
Coefficients: -15.692Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_102857.]: Samplename: 25
Root: 26.346
--> Root in between the borders! Added to results.
Coefficients: -21.804Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_102857.]: Samplename: 37.5
Root: 35.332
--> Root in between the borders! Added to results.
Coefficients: -33.2485Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_102857.]: Samplename: 50
Root: 50.228
--> Root in between the borders! Added to results.
Coefficients: -46.704Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_102857.]: Samplename: 62.5
Root: 65.055
--> Root in between the borders! Added to results.
Coefficients: -55.636Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_102857.]: Samplename: 75
Root: 73.641
--> Root in between the borders! Added to results.
Coefficients: -71.3493333333333Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_102857.]: Samplename: 87.5
Root: 86.903
--> Root in between the borders! Added to results.
Coefficients: -89.46Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_102857.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102857.]: Solving cubic regression for CpG#7
Coefficients: 0Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_102857.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.18066666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_102857.]: Samplename: 12.5
Root: 8.108
--> Root in between the borders! Added to results.
Coefficients: -10.05Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_102857.]: Samplename: 25
Root: 21.288
--> Root in between the borders! Added to results.
Coefficients: -16.236Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_102857.]: Samplename: 37.5
Root: 36.173
--> Root in between the borders! Added to results.
Coefficients: -24.8165Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_102857.]: Samplename: 50
Root: 54.247
--> Root in between the borders! Added to results.
Coefficients: -32.75Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_102857.]: Samplename: 62.5
Root: 67.087
--> Root in between the borders! Added to results.
Coefficients: -39.954Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_102857.]: Samplename: 75
Root: 76.377
--> Root in between the borders! Added to results.
Coefficients: -42.9206666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_102857.]: Samplename: 87.5
Root: 79.728
--> Root in between the borders! Added to results.
Coefficients: -66.008Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_102857.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102857.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102857.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102857.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102857.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102857.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102857.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102857.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102857.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102857.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102857.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_102857.]: Solving cubic regression for CpG#9
Coefficients: 0Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_102857.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_102857.]: Samplename: 12.5
Root: 8.93
--> Root in between the borders! Added to results.
Coefficients: -13.716Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_102857.]: Samplename: 25
Root: 24.492
--> Root in between the borders! Added to results.
Coefficients: -19.634Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_102857.]: Samplename: 37.5
Root: 35.53
--> Root in between the borders! Added to results.
Coefficients: -30.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_102857.]: Samplename: 50
Root: 52.349
--> Root in between the borders! Added to results.
Coefficients: -41.696Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_102857.]: Samplename: 62.5
Root: 65.528
--> Root in between the borders! Added to results.
Coefficients: -51.9135Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_102857.]: Samplename: 75
Root: 74.87
--> Root in between the borders! Added to results.
Coefficients: -64.5026666666667Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_102857.]: Samplename: 87.5
Root: 84.256
--> Root in between the borders! Added to results.
Coefficients: -92Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_102857.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102857.]: Solving cubic regression for row_means
Coefficients: 0Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_102857.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.70125925925926Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_102857.]: Samplename: 12.5
Root: 9.866
--> Root in between the borders! Added to results.
Coefficients: -14.126Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_102857.]: Samplename: 25
Root: 24.413
--> Root in between the borders! Added to results.
Coefficients: -21.378Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_102857.]: Samplename: 37.5
Root: 36.177
--> Root in between the borders! Added to results.
Coefficients: -31.7547777777778Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_102857.]: Samplename: 50
Root: 51.091
--> Root in between the borders! Added to results.
Coefficients: -43.9506666666667Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_102857.]: Samplename: 62.5
Root: 65.785
--> Root in between the borders! Added to results.
Coefficients: -53.632Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_102857.]: Samplename: 75
Root: 75.683
--> Root in between the borders! Added to results.
Coefficients: -61.6601481481482Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_102857.]: Samplename: 87.5
Root: 82.966
--> Root in between the borders! Added to results.
Coefficients: -83.9631111111111Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_102857.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102857.]: ### Starting with regression calculations ###
[20250404_102857.]: Entered 'regression_type1'-Function
[20250404_102858.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_102859.]: Logging df_agg: CpG#1
[20250404_102859.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102859.]: c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_102859.]: Entered 'hyperbolic_regression'-Function
[20250404_102859.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102900.]: Entered 'cubic_regression'-Function
[20250404_102900.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102900.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_102900.]: Logging df_agg: CpG#2
[20250404_102900.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102900.]: c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_102900.]: Entered 'hyperbolic_regression'-Function
[20250404_102900.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102901.]: Entered 'cubic_regression'-Function
[20250404_102901.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102901.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_102901.]: Logging df_agg: CpG#3
[20250404_102901.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102901.]: c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_102901.]: Entered 'hyperbolic_regression'-Function
[20250404_102901.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102902.]: Entered 'cubic_regression'-Function
[20250404_102902.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102902.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_102902.]: Logging df_agg: CpG#4
[20250404_102902.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102902.]: c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_102902.]: Entered 'hyperbolic_regression'-Function
[20250404_102902.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102902.]: Entered 'cubic_regression'-Function
[20250404_102902.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102903.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_102903.]: Logging df_agg: CpG#5
[20250404_102903.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102903.]: c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_102903.]: Entered 'hyperbolic_regression'-Function
[20250404_102903.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102903.]: Entered 'cubic_regression'-Function
[20250404_102903.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102859.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_102900.]: Logging df_agg: CpG#6
[20250404_102900.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102900.]: c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_102900.]: Entered 'hyperbolic_regression'-Function
[20250404_102900.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102900.]: Entered 'cubic_regression'-Function
[20250404_102900.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102901.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_102901.]: Logging df_agg: CpG#7
[20250404_102901.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102901.]: c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_102901.]: Entered 'hyperbolic_regression'-Function
[20250404_102901.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102901.]: Entered 'cubic_regression'-Function
[20250404_102901.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102901.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_102901.]: Logging df_agg: CpG#8
[20250404_102901.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102901.]: c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_102901.]: Entered 'hyperbolic_regression'-Function
[20250404_102901.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102902.]: Entered 'cubic_regression'-Function
[20250404_102902.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102902.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_102902.]: Logging df_agg: CpG#9
[20250404_102902.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102902.]: c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_102902.]: Entered 'hyperbolic_regression'-Function
[20250404_102902.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102902.]: Entered 'cubic_regression'-Function
[20250404_102902.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102903.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_102903.]: Logging df_agg: row_means
[20250404_102903.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_102903.]: c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_102903.]: Entered 'hyperbolic_regression'-Function
[20250404_102903.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102903.]: Entered 'cubic_regression'-Function
[20250404_102903.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_102905.]: Entered 'solving_equations'-Function
[20250404_102905.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 78.9856894800976
[20250404_102905.]: Samplename: Sample#1
Root: 78.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 31.2695317984092
[20250404_102905.]: Samplename: Sample#10
Root: 31.27
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.7015782380441
[20250404_102905.]: Samplename: Sample#2
Root: 42.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.8152127901709
[20250404_102905.]: Samplename: Sample#3
Root: 57.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.2334360674289
[20250404_102905.]: Samplename: Sample#4
Root: 11.233
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.5293831001518
[20250404_102905.]: Samplename: Sample#5
Root: 23.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7706743072545
[20250404_102905.]: Samplename: Sample#6
Root: 24.771
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.3953425213349
[20250404_102905.]: Samplename: Sample#7
Root: 46.395
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.45071436915
[20250404_102905.]: Samplename: Sample#8
Root: 84.451
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.41337105576252
[20250404_102905.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.413
--> '-10 < root < 0' --> substitute 0
[20250404_102905.]: Solving cubic regression for CpG#2
Coefficients: -59.7333333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#1
Root: 76.346
--> Root in between the borders! Added to results.
Coefficients: -19.048Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#10
Root: 31.371
--> Root in between the borders! Added to results.
Coefficients: -27.8783333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#2
Root: 43.142
--> Root in between the borders! Added to results.
Coefficients: -41.795Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#3
Root: 59.121
--> Root in between the borders! Added to results.
Coefficients: -2.21Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#4
Root: 4.128
--> Root in between the borders! Added to results.
Coefficients: -11.665Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#5
Root: 20.292
--> Root in between the borders! Added to results.
Coefficients: -10.08Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#6
Root: 17.745
--> Root in between the borders! Added to results.
Coefficients: -26.488Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#7
Root: 41.383
--> Root in between the borders! Added to results.
Coefficients: -70.532Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#8
Root: 85.378
--> Root in between the borders! Added to results.
Coefficients: -1.13Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102905.]: Samplename: Sample#9
Root: 2.127
--> Root in between the borders! Added to results.
[20250404_102905.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.5474014641742
[20250404_102905.]: Samplename: Sample#1
Root: 74.547
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3579002775045
[20250404_102905.]: Samplename: Sample#10
Root: 28.358
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.6085496577593
[20250404_102905.]: Samplename: Sample#2
Root: 42.609
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.3286114696456
[20250404_102905.]: Samplename: Sample#3
Root: 56.329
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.99034441243248
[20250404_102905.]: Samplename: Sample#4
Root: 7.99
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7023143744962
[20250404_102905.]: Samplename: Sample#5
Root: 24.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.8868798900698
[20250404_102905.]: Samplename: Sample#6
Root: 26.887
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.8318233973603
[20250404_102905.]: Samplename: Sample#7
Root: 44.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6737871528405
[20250404_102905.]: Samplename: Sample#8
Root: 84.674
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.26200732612128
[20250404_102905.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.262
--> '-10 < root < 0' --> substitute 0
[20250404_102905.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 75.8433680333876
[20250404_102905.]: Samplename: Sample#1
Root: 75.843
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.0603248948201
[20250404_102905.]: Samplename: Sample#10
Root: 29.06
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0355928114108
[20250404_102905.]: Samplename: Sample#2
Root: 44.036
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.7751115686327
[20250404_102905.]: Samplename: Sample#3
Root: 58.775
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0319154866029
[20250404_102905.]: Samplename: Sample#4
Root: 11.032
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.9948971650737
[20250404_102905.]: Samplename: Sample#5
Root: 22.995
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.9415139419957
[20250404_102905.]: Samplename: Sample#6
Root: 27.942
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.4874049425657
[20250404_102905.]: Samplename: Sample#7
Root: 42.487
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6802730343613
[20250404_102905.]: Samplename: Sample#8
Root: 84.68
--> Root in between the borders! Added to results.
Hyperbolic solved: 3.00887785677921
[20250404_102905.]: Samplename: Sample#9
Root: 3.009
--> Root in between the borders! Added to results.
[20250404_102905.]: Solving cubic regression for CpG#5
Coefficients: -47.8373333333333Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -13.588Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#10
Root: 27.212
--> Root in between the borders! Added to results.
Coefficients: -25.3211428571429Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#2
Root: 44.85
--> Root in between the borders! Added to results.
Coefficients: -32.064Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#3
Root: 53.741
--> Root in between the borders! Added to results.
Coefficients: -4.074Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#4
Root: 9.444
--> Root in between the borders! Added to results.
Coefficients: -11.434Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#5
Root: 23.55
--> Root in between the borders! Added to results.
Coefficients: -13.294Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#6
Root: 26.722
--> Root in between the borders! Added to results.
Coefficients: -24.288Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#7
Root: 43.42
--> Root in between the borders! Added to results.
Coefficients: -63.134Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#8
Root: 88.215
--> Root in between the borders! Added to results.
Coefficients: 0.0360000000000005Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102905.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.091
--> '-10 < root < 0' --> substitute 0
[20250404_102905.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2200555510382
[20250404_102905.]: Samplename: Sample#1
Root: 79.22
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2526528381147
[20250404_102905.]: Samplename: Sample#10
Root: 30.253
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.9196854329573
[20250404_102905.]: Samplename: Sample#2
Root: 41.92
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8984354098215
[20250404_102905.]: Samplename: Sample#3
Root: 56.898
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.81576403111374
[20250404_102905.]: Samplename: Sample#4
Root: 8.816
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.6921622783918
[20250404_102905.]: Samplename: Sample#5
Root: 18.692
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9815019073132
[20250404_102905.]: Samplename: Sample#6
Root: 29.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8875178508205
[20250404_102905.]: Samplename: Sample#7
Root: 42.888
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.6303733181195
[20250404_102905.]: Samplename: Sample#8
Root: 86.63
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.38997712955107
[20250404_102905.]: Samplename: Sample#9
Root: 1.39
--> Root in between the borders! Added to results.
[20250404_102905.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 77.5278331978133
[20250404_102905.]: Samplename: Sample#1
Root: 77.528
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.0895401031897
[20250404_102905.]: Samplename: Sample#10
Root: 27.09
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.4382794903846
[20250404_102905.]: Samplename: Sample#2
Root: 48.438
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.8815971416453
[20250404_102905.]: Samplename: Sample#3
Root: 58.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.3295768294236
[20250404_102905.]: Samplename: Sample#4
Root: 13.33
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9816196357542
[20250404_102905.]: Samplename: Sample#5
Root: 26.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.9612159665911
[20250404_102905.]: Samplename: Sample#6
Root: 30.961
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.7456547820365
[20250404_102905.]: Samplename: Sample#7
Root: 45.746
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6033538318025
[20250404_102905.]: Samplename: Sample#8
Root: 84.603
--> Root in between the borders! Added to results.
Hyperbolic solved: -2.87380061592101
[20250404_102905.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.874
--> '-10 < root < 0' --> substitute 0
[20250404_102905.]: Solving cubic regression for CpG#8
Coefficients: -55.3573333333333Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#1
Root: 72.421
--> Root in between the borders! Added to results.
Coefficients: -17.574Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#10
Root: 28.533
--> Root in between the borders! Added to results.
Coefficients: -22.9425714285714Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#2
Root: 35.766
--> Root in between the borders! Added to results.
Coefficients: -42.849Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#3
Root: 59.36
--> Root in between the borders! Added to results.
Coefficients: -4.604Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#4
Root: 8.481
--> Root in between the borders! Added to results.
Coefficients: -11.389Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#5
Root: 19.519
--> Root in between the borders! Added to results.
Coefficients: -25.784Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#6
Root: 39.413
--> Root in between the borders! Added to results.
Coefficients: -30.746Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#7
Root: 45.53
--> Root in between the borders! Added to results.
Coefficients: -66.912Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#8
Root: 83.654
--> Root in between the borders! Added to results.
Coefficients: 3.176Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102905.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -6.535
--> '-10 < root < 0' --> substitute 0
[20250404_102905.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 80.5486410672961
[20250404_102906.]: Samplename: Sample#1
Root: 80.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.810468482135
[20250404_102906.]: Samplename: Sample#10
Root: 27.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.2641649294309
[20250404_102906.]: Samplename: Sample#2
Root: 46.264
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.1903653427228
[20250404_102906.]: Samplename: Sample#3
Root: 57.19
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.63886339746086
[20250404_102906.]: Samplename: Sample#4
Root: 8.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2162393845509
[20250404_102906.]: Samplename: Sample#5
Root: 24.216
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.6394430638471
[20250404_102906.]: Samplename: Sample#6
Root: 39.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.3080887012493
[20250404_102906.]: Samplename: Sample#7
Root: 44.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.3259098830063
[20250404_102906.]: Samplename: Sample#8
Root: 87.326
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.17959639730045
[20250404_102906.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.18
--> '-10 < root < 0' --> substitute 0
[20250404_102906.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 76.7568961192102
[20250404_102906.]: Samplename: Sample#1
Root: 76.757
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.8326630603664
[20250404_102906.]: Samplename: Sample#10
Root: 28.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.0145327025204
[20250404_102906.]: Samplename: Sample#2
Root: 43.015
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.6144798147902
[20250404_102906.]: Samplename: Sample#3
Root: 57.614
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.86517972238162
[20250404_102906.]: Samplename: Sample#4
Root: 8.865
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.1849817550475
[20250404_102906.]: Samplename: Sample#5
Root: 22.185
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.1973843238972
[20250404_102906.]: Samplename: Sample#6
Root: 29.197
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.9174258632975
[20250404_102906.]: Samplename: Sample#7
Root: 43.917
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6607695784409
[20250404_102906.]: Samplename: Sample#8
Root: 85.661
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.551158207550385
[20250404_102906.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.551
--> '-10 < root < 0' --> substitute 0
[20250404_102906.]: Entered 'solving_equations'-Function
[20250404_102906.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_102906.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_102906.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_102906.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_102906.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_102906.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_102906.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_102906.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102906.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102906.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102906.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102906.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102906.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102906.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102906.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102906.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102906.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_102906.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102906.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_102906.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_102906.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_102906.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_102906.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_102906.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_102906.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_102906.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102906.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102906.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_102906.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_102906.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_102906.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_102906.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_102906.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_102906.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_102906.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102906.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102906.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102906.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102906.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102906.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102906.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102906.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102906.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102906.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_102906.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_102906.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_102906.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_102906.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_102906.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_102906.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_102906.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_102906.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_102906.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102906.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102906.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_102906.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_102906.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_102906.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_102906.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_102906.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_102906.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_102906.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102906.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102906.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102906.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102906.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102906.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102906.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102906.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102906.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102906.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_102906.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_102906.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_102906.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_102906.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_102906.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_102906.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_102906.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_102906.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_102906.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102906.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_102906.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_102906.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_102906.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_102906.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_102906.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_102906.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_102906.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_102906.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_102906.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_102906.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[20250404_103059.]: Entered 'clean_dt'-Function
[20250404_103059.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_103059.]: got experimental data
[20250404_103059.]: Entered 'clean_dt'-Function
[20250404_103059.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data)
[20250404_103059.]: got experimental data
[20250404_103100.]: Entered 'clean_dt'-Function
[20250404_103100.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_103100.]: got calibration data
[20250404_103100.]: Entered 'clean_dt'-Function
[20250404_103100.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_103100.]: got calibration data
[20250404_103100.]: Entered 'hyperbolic_regression'-Function
[20250404_103100.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• On CRAN (4): 'test-algorithm_minmax_FALSE.R:80:5',
'test-algorithm_minmax_TRUE.R:76:5', 'test-hyperbolic.R:27:5',
'test-lints.R:12:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-algorithm_minmax_FALSE_re.R:170:5'): algorithm test, type 1, minmax = FALSE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_FALSE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-algorithm_minmax_TRUE_re.R:170:5'): algorithm test, type 1, minmax = TRUE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_TRUE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:17:5'): test normal function of file import of type 1 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:17:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:65:5'): test normal function of file import of type 2 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:65:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-create_aggregated.R:19:5'): test functioning of aggregated function ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-create_aggregated.R:19:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
Error: Test failures
Execution halted
Error in deferred_run(env) : could not find function "deferred_run"
Calls: <Anonymous>
Flavor: r-devel-linux-x86_64-fedora-gcc