missForest.par {ADAPTS}

Use parallel missForest to impute missing values.

Description

This wrapper is helpful because missForest crashes if you have more cores than variables. This will default to no parellelization for Windows

newMatrix <- missForest.par(dataMat)

Usage

missForest.par(dataMat, parallelize = "variables")

Arguments

dataMat

Columns are features, Rows examples. The data with NA values. 'xmis' in missForest

parallelize

split on 'forests' or 'variables' (DEFAULT: 'variables')

Value

a matrix including imputed values

Examples

library(ADAPTS)
LM22 <- ADAPTS::LM22
LM22[2,3] <- as.numeric(NA) #Make some missing data to impute
LM22.imp <- missForest.par(LM22)