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 or survey object containing the data
`family` | family argument for "glm" ("AUTO" tries to choose automatically, only really tested option!!!)
`robust` | TRUE/FALSE if robust regression should be used
`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.

### 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 4.8.0 Index]