VIM (version 4.6.0)

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 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.

Value

the imputed data set.

Details

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

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)