randomUniformForest (version 1.1.2)

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,
	categoricalvariablesidx = NULL,
	NAgrep = "", 
	maxClasses = floor(0.01*min(3000, nrow(X))+2),
	threads = "auto")

Value

• a matrix or a data frame containing completed 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 the 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. Note that the function will try to render exactly the same structure than the one in the original dataset.

Examples

## not run
## get same example as rfImpute() function 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