# R-miss-tastic

A resource website on missing values - Methods and references for managing missing data

missForest

##### Category:

Single Imputation

##### Use-Cases:

Single Imputation of continuous and/or categorical data.

##### Description:

The function ‘missForest’ in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.

#### Last update:

##### Algorithms:
• missForest (randomForest)

none

data.frame

##### Example:
library(missForest)
library(VIM)

# Load sleep data from VIM package as example
data(sleep, package = "VIM")
print("before imputation")
summary(sleep)

# Perform imputation
erg <- missForest(sleep)
print("after imputation")
summary(erg\$ximp)



Here you can have a interactive look at the example: https://rdrr.io/snippets/embedding/