The goal of imputeGeneric is to ease the implementation of imputation functions.
You can install the development version of imputeGeneric from GitHub with:
The aim of imputeGeneric is to make the implementation and usage of imputation methods easier. The main function of the package is impute_iterative(). This function can turn any parsnip model into an imputation method. Furthermore, other customized approaches can be used in a general imputation framework. For more information, see the documentations of impute_iterative(), impute_supervised(), impute_unsupervised() and the following examples.
The use of a parsnip model for imputation is demonstrated using regression trees from the rpart package via parsnip (decision_tree("regression")). First, a data set with missing values is created. Then, this data set is imputed once with regression trees using only completely observed rows and columns for the model building.
library(imputeGeneric)
library(parsnip)
# create data set
set.seed(123)
ds_mis <- data.frame(X = rnorm(100), Y = rnorm(100))
ds_mis$Z <- 5 + 2* ds_mis$X + ds_mis$Y + rnorm(100)
ds_mis$Z[sample.int(100, 30)] <- NA
ds_mis$Y[sample.int(100, 20)] <- NA
# impute data set
ds_imp <- impute_iterative(ds_mis, decision_tree("regression"), max_iter = 1)
anyNA(ds_imp)
#> [1] FALSETo use other parsnip models instead of regression trees, only the model_spec_parsnip argument must be altered. E.g. for linear regression instead of regression trees use linear_reg().
Many aspects of the imputation can be specified and customized. The missing values can be initially imputed e.g. with per column mean values (initial_imputation_fun = missMethods::impute_mean). In addition, all objects and columns can be used for the imputation models (rows_used_for_imputation = "all" and cols_used_for_imputation = "all"). Furthermore, the imputation can be iterative. The iteration will be stopped, if either the difference between two imputed data sets falls below a threshold (stop_fun = stop_ds_difference, stop_fun_args = list(eps = 0.1)) or the maximum number of iterations (max_iter = 5) is reached.