rangerImpute: Random Forest Imputation

In VIM: Visualization and Imputation of Missing Values

Random Forest Imputation

Description

Impute missing values based on random-forest models via vimpute().

Usage

rangerImpute(
  formula,
  data,
  imp_var = TRUE,
  imp_suffix = "imp",
  ...,
  verbose = FALSE,
  median = FALSE
)

Arguments

formula	model formula for the imputation
data	A data.frame containing the data
imp_var	TRUE/FALSE if a TRUE/FALSE variables for each imputed variable should be created show the imputation status
imp_suffix	suffix used for TF imputation variables
...	Additional ranger hyperparameters (e.g. num.trees, mtry, min.node.size), forwarded to the ranger learner via vimpute().
verbose	Show the number of observations used for training and evaluating the RF-Model.
median	TRUE/FALSE. If TRUE, ranger regression predictions are aggregated tree-wise using the median (via vimpute()).

Value

the imputed data set.

See Also

Other imputation methods: hotdeck(), impPCA(), imputeCellEM(), imputeCellIRMI(), imputeCellM(), imputeCellMCD(), imputeCellwise(), imputeRobust(), imputeRobustChain(), irmi(), kNN(), matchImpute(), medianSamp(), regressionImp(), sampleCat(), vimmi, vimpute(), xgboostImpute()

Examples

data(sleep)
rangerImpute(Dream+NonD~BodyWgt+BrainWgt,data=sleep)

VIM documentation built on Sept. 2, 2026, 5:07 p.m.