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
`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.
`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

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

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 4.0.0 Index]