rangerImpute: Random Forest Imputation

In VIM: Visualization and Imputation of Missing Values

Random Forest Imputation

Description

Impute missing values based on a random forest model using ranger::ranger()

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
...	Arguments passed to ranger::ranger()
verbose	Show the number of observations used for training and evaluating the RF-Model. This parameter is also passed down to ranger::ranger() to show computation status.
median	Use the median (rather than the arithmetic mean) to average the values of individual trees for a more robust estimate.

Value

the imputed data set.

See Also

Other imputation methods: hotdeck(), impPCA(), irmi(), kNN(), matchImpute(), medianSamp(), regressionImp(), sampleCat()

Examples

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

VIM documentation built on Aug. 25, 2022, 5:07 p.m.