The OTrecod package gives access to a set of original functions dedicated to data fusion.
From two separate data sources with no overlapping units, sharing only a set of common variables X and a same target information not jointly observed in a same encoding from one data source to another (Y in A and Z in B), the functions OT_outcome and OT_joint aim at providing users a complete synthetic database where the missing information is available for every unit.
This recoding problem is solved using the optimal transportation theory which provides a map that transfers the joint distribution of the first target variable and X to the joint distribution of the second one and X, or inversely. Algorithms used in these two functions come from the references (1) and (2).
If the package OTrecod is not installed in their current R versions, users can install it by following the standard instruction:
install.packages("OTrecod")Obviously, each time an R session is opened, the OTrecod library must be loaded with:
library(OTrecod)Moreover, the development version of OTrecod can be installed actually from GitHub with:
# Install development version from GitHub
devtools::install_github("otrecoding/OTrecod")
The available databases called tab_test and simu_data correspond to overlayed databases used as examples in the documentation of all the functions. Their structures can help users understanding the database structure expected as input argument of the functions OT_outcome and OT_joint. The first rows of the two overlayed data sources of simu_data are visualized as follows to inform about the expected database structure:
data(simu_data)
dim(simu_data)
[1] 700 8
simu_data[c(1:5,301:305),]
DB Yb1 Yb2 Gender Treatment Dosage Smoking Age
1 A [40-60[ NA Female Trt A Dos 3 YES 65.44273
2 A [20-40] NA Male <NA> Dos 2 NO 51.78596
3 A [40-60[ NA Female Placebo Dos 2 YES 49.10844
4 A [40-60[ NA Female Trt B Dos 4 <NA> 56.43524
5 A [40-60[ NA Female Trt A Dos 4 YES 44.77365
301 B <NA> 5 Female Placebo Dos 2 YES 44.58233
302 B <NA> 1 Female Trt B Dos 4 <NA> 65.23921
303 B <NA> 2 Female Placebo <NA> NO 51.64228
304 B <NA> 2 Female Trt A <NA> NO 50.15125
305 B <NA> 1 Female Trt B Dos 4 YES 61.53242The first column called DB corresponds here to the database identifier (two data sources called here 1 and 2 with the data source 1 placed above the data source 2). The second column called Yb1 is the target variable of the data source 1. The values of Yb1 in the data source 2 are missing and will be predicted using an optimal transportation algorithm integrated in one of the two functions called OT_outcome and OT_joint. In the same way, the variable Yb2 (third column) is the target variable of the data source 2 whose values in 1 are unknown. These missing values can also be predicted using OT_outcome and OT_joint.
The presence of these three variables is essential in any database dedicated to datafusion in the OTrecod package whatevever their names and whatever their orders in the database. The following columns correspond to shared variables of any type, complete or not. Note that continuous variables (like age in years) are not allowed with the OT_joint function.
Support functions are available in the package (merge_dbs, imput_cov) to assist user in this preparation.
Finally, the supplementary datasets api29 and api35 are simple datasets extracted from the API program (https://www.cde.ca.gov/re/pr/api.asp) to allow users to practice with convenient databases.
Among the available functions, the OTrecod package provides a set of support functions to assist users in each step of their data fusion projects.
The merge_dbs function is a pre-process data fusion function dedicated to the harmonization of two data sources. By default, variables (not target variables) with same labels are considered as shared between the two databases. The merge_dbs function detects potential discrepancies between the variables before merging by:
merge_dbs = function(DB1, DB2, row_ID1 = NULL, row_ID2 = NULL, NAME_Y, NAME_Z, order_levels_Y = levels(DB1[, NAME_Y]), order_levels_Z = levels(DB2[, NAME_Z]), ordinal_DB1 = NULL, ordinal_DB2 = NULL,
impute = "NO", R_MICE = 5, NCP_FAMD = 3, seed_choice = sample(1:1000000, 1))The merge_dbs function notably provides in output an unique database, result of the overlayed of the two initial data sources, in the structure expected by the OT_outcome and OT_joint functions.
The select_pred function is a pre-process data fusion function dedicated to the selection of matching variables. This selection is essential when the initial set of shared variables is important, but also because the choice of predictors greatly influences the quality of the data fusion whatever the optimal transportation algorithms chosen a posteriori.
The call of the select_pred function is actually:select_pred = function(databa,Y = NULL, Z = NULL, ID = 1, OUT = "Y", quanti = NULL, nominal = NULL, ordinal = NULL, logic = NULL,
convert_num = NULL, convert_class = NULL, thresh_cat = 0.30, thresh_num = 0.70, thresh_Y = 0.20,
RF = TRUE, RF_ntree = 500, RF_condi = FALSE, RF_condi_thr = 0.20, RF_SEED = sample(1:1000000, 1))
The verif_OT function is a post-process data fusion function dedicated to the validation of the fusion. The function provides a set of tools to assess the quality of the optimal transportation recoding proposed by the algorithms to predict the missing information of the target variables in one or both datasources.
The call of the verif_OT function is actually:
verif_OT = function(ot_out, group.class = FALSE, ordinal = TRUE, stab.prob = FALSE, min.neigb = 1, R = 10, seed.stab = sample(1:1000000, 1))
The OTrecod package provides two algorithms that use optimal transportation theory to solve recoding problems in data fusion contexts (see (1) and (2) for more details). Each algorithm is stored in one function and each function provides in output a unique and synthetic database where the two initial data sources are overlayed and the missing information from only one or both target variables are fully completed.
Each of the two alogorithms also proposed enrichments by relaxing the initial distributional constraints and adding regularization terms as described in (2).
The OT_outcome function can provide individual predictions of the incomplete target variables by considering the recoding problem involving only optimal transportation of outcomes (see (1) and (2) for more details).
The call of the OT_outcome function is:OT_outcome = function(datab, index_DB_Y_Z = 1:3, quanti = NULL, nominal = NULL, ordinal = NULL,logic = NULL,
convert.num = NULL, convert.class = NULL, FAMD.coord = "NO", FAMD.perc = 0.8,
dist.choice = "E", percent.knn = 1, maxrelax = 0, indiv.method = "sequential",
prox.dist = 0.30, solvR = "glpk", which.DB = "BOTH")
The OT_joint function can provide individual predictions of the incomplete target variables by considering the recoding problem involving optimal transportation of shared variables and outcomes (see(2) for more details).
The call of the OT_joint function is:OT_joint = function(datab, index_DB_Y_Z = 1:3, nominal = NULL, ordinal = NULL,logic = NULL,
convert.num = NULL, convert.class = NULL, dist.choice = "E", percent.knn = 1,
maxrelax = 0, lambda.reg = 0.0, prox.X = 0.10, solvR = "glpk", which.DB = "BOTH")
Gares V, Dimeglio C, Guernec G, Fantin F, Lepage B, Korosok MR, savy N (2019). On the use of optimal transportation theory to recode variables and application to database merging. The International Journal of Biostatistics.Volume 16, Issue 1, 20180106, eISSN 1557-4679.
Gares V, Omer J (2020). Regularized optimal transport of covariates and outcomes in data recoding. Journal of the American Statistical Association.