R: Random Forest Imputation

rangerImpute {VIM} | R Documentation

## 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()`, `irmi()`, `kNN()`, `matchImpute()`, `regressionImp()`

### Examples

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

[Package VIM version 6.0.0 Index]