The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

missMethods

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

Installation

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")

Usage

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")).

Example

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.