randomUniformForest (version 1.0.6)

fillNA2.randomUniformForest: Missing values imputation by randomUniformForest

Description

Impute missing values using randomUniformForest. Each variable containing missing values is, in turn, considered as a responses vector, where non-missing values are training responses and missing values, responses to predict.

Usage

fillNA2.randomUniformForest(X, Y = NULL, ntree = 100, 
	mtry = 1, 
	nodesize = 10, 
	NAgrep = "", 
	threads = "auto")

Arguments

X

A data frame or matrix of predictors.

Y

response vector.

ntree

number of trees to grow for each predictor with missing values. Default value usually works, unless one wants a better level of accuracy.

mtry

number of predictors to try for growing the forest. Default value means full randomized trees will be grown.

nodesize

smallest number of observations in each leaf node.

NAgrep

which symbol (in case of data frame), e.g. "?", in addition to "NA" has to be considered as a missing value.

threads

how many logical cores to complete data. Default values will let algorithm use all available cores.

Value

• a matrix or a data frame containing complete values.

Details

Algorithm uses randomUniformForest to complete matrix. At the first step, all missing values are identified and rough imputation is done using most frequent (or median) values for each predictor with missing values. Then, these predictors are, one by one, considered as responses, where training data are ones with non missing values (or roughly fixed) and test sample are data whose values are really missing. There is only one iteration (over all predictors) since this version already requires computing time. Hence, it is strongly recommended to use it only if others imputation models do not work or if speed is not mandatory.

Examples

## not run
## get same example as rfImpute() fonction from randomForest package
# data(iris)
# iris.na <- iris
# set.seed(111)
## artificially drop some data values.
# for (i in 1:4) iris.na[sample(150, sample(20)), i] <- NA

## imputation 
# iris.imputed <- fillNA2.randomUniformForest(iris.na, threads = 1)

## model with imputation
# iris.NAfixed.ruf <- randomUniformForest(Species ~ ., iris.imputed, 
# BreimanBounds = FALSE, threads = 1)
# iris.NAfixed.ruf

## Compare with true data (OOB evaluation)
# iris.ruf <- randomUniformForest(Species ~ ., iris, BreimanBounds = FALSE, threads = 1)
# iris.ruf