Last updated on 2026-05-13 14:50:20 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-fedora-clang | 0.3.1 | 7.00 | 73.92 | 80.92 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 0.3.1 | 62.32 | ERROR | |||
| r-release-macos-arm64 | 0.3.1 | 1.00 | 17.00 | 18.00 | OK | |
| r-release-macos-x86_64 | 0.3.1 | 3.00 | 59.00 | 62.00 | OK | |
| r-release-windows-x86_64 | 0.3.1 | 8.00 | 90.00 | 98.00 | OK | |
| r-oldrel-macos-arm64 | 0.3.1 | 1.00 | 20.00 | 21.00 | OK | |
| r-oldrel-macos-x86_64 | 0.3.1 | 3.00 | 49.00 | 52.00 | OK |
Version: 0.3.1
Check: examples
Result: ERROR
Running examples in ‘accelEE-Ex.R’ failed
The error most likely occurred in:
> ### Name: accelEE-function
> ### Title: Predict energy expenditure for accelerometry data
> ### Aliases: accelEE-function accelEE wrap_2RM crouter15 hildebrand_linear
> ### hildebrand_nonlinear montoye sojourn staudenmayer
>
> ### ** Examples
>
>
> #### Below, note the variations throughout the examples,
> #### showing different ways you can customize the output
>
>
>
> ## Raw acceleration examples:
>
> if (isTRUE(requireNamespace("read.gt3x", quietly = TRUE))) {
+
+ f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
+ d <- stats::setNames(
+ read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE),
+ c("Timestamp", "Accelerometer_X", "Accelerometer_Y", "Accelerometer_Z")
+ )[1:30000, ]
+
+ utils::head(
+ accelEE(
+ d, "Hibbing 2018", algorithm = 1,
+ site = c("Left Wrist", "Right Wrist"),
+ warn_high_low = FALSE, shrink_output = FALSE
+ )
+ )
+
+ utils::head(
+ accelEE(
+ d, c("Hildebrand Linear", "Hildebrand Non-Linear"), age = "adult",
+ monitor = "ActiGraph", location = "Wrist", warn_high_low = FALSE,
+ ee_vars = c("METs", "kcal"), output_epoch = "60 sec"
+ )
+ )
+
+ accelEE(
+ d, c(
+ "Montoye 2017", "Staudenmayer Linear",
+ "Staudenmayer Random Forest"
+ ), side = "left", ee_vars = "VO2", combine = FALSE
+ )
+
+ }
*** caught segfault ***
address 0x1, cause 'memory not mapped'
Traceback:
1: predict.nnet(model, newdata = d)
2: stats::predict(model, newdata = d)
3: check_values(stats::predict(model, newdata = d), min_mets, max_mets, label, "MET", "MET(s)", warn_high_low)
4: .Call(dplyr_mask_eval_all_mutate, quo, private)
5: eval()
6: mask$eval_all_mutate(quo)
7: mutate_col(dots[[i]], data, mask, new_columns)
8: withCallingHandlers(for (i in seq_along(dots)) { poke_error_context(dots, i, mask = mask) context_poke("column", old_current_column) new_columns <- mutate_col(dots[[i]], data, mask, new_columns)}, error = dplyr_error_handler(dots = dots, mask = mask, bullets = mutate_bullets, error_call = error_call, error_class = "dplyr:::mutate_error"), warning = dplyr_warning_handler(state = warnings_state, mask = mask, error_call = error_call))
9: mutate_cols(.data, dplyr_quosures(...), by)
10: mutate.data.frame(., `:=`(!!as.name(out_name), check_values(stats::predict(model, newdata = d), min_mets, max_mets, label, "MET", "MET(s)", warn_high_low)))
11: dplyr::mutate(., `:=`(!!as.name(out_name), check_values(stats::predict(model, newdata = d), min_mets, max_mets, label, "MET", "MET(s)", warn_high_low)))
12: d %>% dplyr::mutate(`:=`(!!as.name(out_name), check_values(stats::predict(model, newdata = d), min_mets, max_mets, label, "MET", "MET(s)", warn_high_low)))
13: predict_model(d, out_name, model, label, min_mets, max_mets, warn_high_low)
14: predict_montoye(., "METs_left_wrist", "left", side, EE.Data::montoye_lw, "Montoye Left Wrist", min_mets, max_mets, warn_high_low)
15: predict_montoye(., "METs_right_wrist", "right", side, EE.Data::montoye_rw, "Montoye Right Wrist", min_mets, max_mets, warn_high_low)
16: dplyr::rename_with(., function(x, met_name, tag) { gsub(met_name, "", x) %>% paste0(tag, "_METs_", .)}, dplyr::matches(met_name), met_name = met_name, tag = tag)
17: is.factor(x)
18: gsub("[_.-]+", "_", names(.))
19: stats::setNames(., gsub("[_.-]+", "_", names(.)))
20: dplyr::mutate(., dplyr::across(dplyr::contains("METs"), ~check_values(.x, minimum = min_mets, maximum = max_mets, label = gsub("[\\._\\-]+", "", toupper(tag)), variable = "MET", units = "MET(s)", warn_high_low = warn_high_low)), dplyr::across(dplyr::contains("METs"), ~.x * met_mlkgmin, .names = "{gsub(\"METs\", \"vo2_mlkgmin\", .col)}"), dplyr::across(dplyr::contains("vo2_mlkgmin"), ~.x/1000 * PAutilities::get_kcal_vo2_conversion(RER, "Lusk"), .names = "{gsub(\"vo2_mlkgmin\", \"kcal_kgmin\", .col)}"))
21: is.factor(x)
22: gsub("_+$", "", names(.))
23: stats::setNames(., gsub("_+$", "", names(.)))
24: d %>% dplyr::rename_with(function(x, met_name, tag) { gsub(met_name, "", x) %>% paste0(tag, "_METs_", .)}, dplyr::matches(met_name), met_name = met_name, tag = tag) %>% stats::setNames(., gsub("[_.-]+", "_", names(.))) %>% dplyr::mutate(dplyr::across(dplyr::contains("METs"), ~check_values(.x, minimum = min_mets, maximum = max_mets, label = gsub("[\\._\\-]+", "", toupper(tag)), variable = "MET", units = "MET(s)", warn_high_low = warn_high_low)), dplyr::across(dplyr::contains("METs"), ~.x * met_mlkgmin, .names = "{gsub(\"METs\", \"vo2_mlkgmin\", .col)}"), dplyr::across(dplyr::contains("vo2_mlkgmin"), ~.x/1000 * PAutilities::get_kcal_vo2_conversion(RER, "Lusk"), .names = "{gsub(\"vo2_mlkgmin\", \"kcal_kgmin\", .col)}")) %>% stats::setNames(., gsub("_+$", "", names(.)))
25: met_expand(., "METs", "montoye", met_mlkgmin, min_mets, max_mets, RER, warn_high_low)
26: d %>% predict_montoye("METs_left_wrist", "left", side, EE.Data::montoye_lw, "Montoye Left Wrist", min_mets, max_mets, warn_high_low) %>% predict_montoye("METs_right_wrist", "right", side, EE.Data::montoye_rw, "Montoye Right Wrist", min_mets, max_mets, warn_high_low) %>% met_expand("METs", "montoye", met_mlkgmin, min_mets, max_mets, RER, warn_high_low)
27: montoye(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...)
28: lapply(X = X, FUN = FUN, ...)
29: sapply(., switch, `Crouter 2006` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2006", ..., met_name = "METs", tag = "crouter06"), `Crouter 2010` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2010", ..., met_name = "METs", tag = "crouter10"), `Crouter 2012` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2012", ..., met_name = "METs", tag = "crouter12"), `Crouter 2015` = crouter15(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, ...), `Hibbing 2018` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Hibbing 2018", ..., met_name = "METs", tag = "hibbing18"), `Hildebrand Linear` = hildebrand_linear(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), `Hildebrand Non-Linear` = hildebrand_nonlinear(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), `Montoye 2017` = montoye(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), SIP = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "SIP", ..., met_name = "METs", tag = "SIP"), `Sojourn 1x` = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "Sojourn 1x", ..., met_name = "METs", tag = "soj_1x"), `Sojourn 3x` = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "Sojourn 3x", ..., met_name = "METs", tag = "soj_3x"), `Staudenmayer Linear` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = "METs_lm"), `Staudenmayer Random Forest` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = "METs_rf"), `Staudenmayer Both` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = c("METs_lm", "METs_rf")), stop("Invalid value passed for `method` argument:", " see ?args(accelEE::accelEE) for options", call. = FALSE), simplify = FALSE)
30: is.factor(x)
31: gsub("^Staudenmayer Both$", "Staudenmayer", names(.))
32: stats::setNames(., gsub("^Staudenmayer Both$", "Staudenmayer", names(.)))
33: method %>% sapply(switch, `Crouter 2006` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2006", ..., met_name = "METs", tag = "crouter06"), `Crouter 2010` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2010", ..., met_name = "METs", tag = "crouter10"), `Crouter 2012` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2012", ..., met_name = "METs", tag = "crouter12"), `Crouter 2015` = crouter15(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, ...), `Hibbing 2018` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Hibbing 2018", ..., met_name = "METs", tag = "hibbing18"), `Hildebrand Linear` = hildebrand_linear(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), `Hildebrand Non-Linear` = hildebrand_nonlinear(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), `Montoye 2017` = montoye(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), SIP = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "SIP", ..., met_name = "METs", tag = "SIP"), `Sojourn 1x` = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "Sojourn 1x", ..., met_name = "METs", tag = "soj_1x"), `Sojourn 3x` = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "Sojourn 3x", ..., met_name = "METs", tag = "soj_3x"), `Staudenmayer Linear` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = "METs_lm"), `Staudenmayer Random Forest` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = "METs_rf"), `Staudenmayer Both` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = c("METs_lm", "METs_rf")), stop("Invalid value passed for `method` argument:", " see ?args(accelEE::accelEE) for options", call. = FALSE), simplify = FALSE) %>% stats::setNames(., gsub("^Staudenmayer Both$", "Staudenmayer", names(.)))
34: accelEE(d, c("Montoye 2017", "Staudenmayer Linear", "Staudenmayer Random Forest"), side = "left", ee_vars = "VO2", combine = FALSE)
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-devel-linux-x86_64-fedora-gcc