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Showing content from https://github.com/torockel/missMethods below:

torockel/missMethods: Methods for Missing Data

The goal of missMethods is to make the creation and handling of missing data as well as the evaluation of missing data methods easier.

You can install the released version of missMethods from CRAN with:

install.packages("missMethods")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("torockel/missMethods")

missMethods mainly provides three types of functions:

• delete_ functions for generating missing values
• impute_ functions for imputing missing values
• evaluate_ functions for evaluating missing data methods

Run help(package = "missMethods") to see all functions. More details for the delete_ functions are given in a vignette (run vignette("Generating-missing-values")).

This is a very basic workflow to generate missing values, impute the generated missing values and evaluate the imputation result:

library(missMethods)
set.seed(123)
ds_comp <- data.frame(X = rnorm(100), Y = rnorm(100))
ds_mis <- delete_MCAR(ds_comp, 0.3)
ds_imp <- impute_mean(ds_mis)
evaluate_imputed_values(ds_imp, ds_comp, "RMSE")
#> [1] 0.5328238

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