VIM: regressionImp – R documentation

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Documentation

VIM

regressionImp

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.

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)

VIM

Visualization and Imputation of Missing Values

v6.1.0

GPL (>= 2)

Authors

Matthias Templ [aut, cre], Alexander Kowarik [aut] (<https://orcid.org/0000-0001-8598-4130>), Andreas Alfons [aut], Gregor de Cillia [aut], Bernd Prantner [ctb], Wolfgang Rannetbauer [aut]

Initial release