regressionImp: Regression Imputation

In VIM: Visualization and Imputation of Missing Values

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(), impPCA(), irmi(), kNN(), matchImpute(), medianSamp(), rangerImpute(), sampleCat()

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