R: Regression Imputation

regressionImp {VIM} | R Documentation

## Regression Imputation

### Description

Impute missing values based on a regression model.

### Usage

    regressionImp(
      formula,
      data,
      family = "AUTO",
      robust = FALSE,
      imp_var = TRUE,
      imp_suffix = "imp",
      mod_cat = FALSE
    )

### Arguments

`formula` | model formula to impute one variable
`data` | A data.frame containing the data
`family` | family argument for `glm()`. `"AUTO"` (the default) tries to choose automatically and is the only really tested option!!!
`robust` | `TRUE`/`FALSE` if robust regression should be used. See details.
`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
`mod_cat` | `TRUE`/`FALSE` if `TRUE` for categorical variables the level with the highest prediction probability is selected, otherwise it is sampled according to the probabilities.

### Details

`lm()` is used for family "normal" and `glm()` for all other families. (robust=TRUE: `lmrob()`, `glmrob()`)

### Value

the imputed data set.

### Author(s)

Alexander Kowarik

### References

A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 1-16.

### See Also

Other imputation methods: `hotdeck()`, `irmi()`, `kNN()`, `matchImpute()`, `rangerImpute()`

### Examples

    data(sleep)
    sleepImp1 <- regressionImp(Dream+NonD~BodyWgt+BrainWgt,data=sleep)
    sleepImp2 <- regressionImp(Sleep+Gest+Span+Dream+NonD~BodyWgt+BrainWgt,data=sleep)

    data(testdata)
    imp_testdata1 <- regressionImp(b1+b2~x1+x2,data=testdata$wna)
    imp_testdata3 <- regressionImp(x1~x2,data=testdata$wna,robust=TRUE)

[Package VIM version 6.0.0 Index]