The goal of imputeGeneric is to ease the implementation of imputation functions.
You can install the development version of imputeGeneric from GitHub with:
# install.packages("devtools")
devtools::install_github("torockel/imputeGeneric")
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] FALSE
To 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()
.
ds_imp_lm <- impute_iterative(ds_mis, linear_reg(), max_iter = 1)
anyNA(ds_imp_lm)
#> [1] FALSE
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.
ds_imp2 <- impute_iterative(
ds_mis, decision_tree("regression"),
initial_imputation_fun = missMethods::impute_mean,
cols_used_for_imputation = "all",
rows_used_for_imputation = "all",
stop_fun = stop_ds_difference,
stop_fun_args = list(eps = 0.1),
max_iter = 5)
anyNA(ds_imp2)
#> [1] FALSE