missForest.par: Use parallel missForest to impute missing values.
In yxinyi2/ADAPTS1.0.3: Automated Deconvolution Augmentation of Profiles for Tissue Specific Cells

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)

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

`dataMat`	Columns are features, Rows examples. The data with NA values. 'xmis' in missForest
`parallelize`	split on 'forests' or 'variables' (DEFAULT: 'variables')

a matrix including imputed values

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

yxinyi2/ADAPTS1.0.3 documentation built on Aug. 5, 2020, 12:09 a.m.

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