VIM (version 6.2.2)

matchImpute: Fast matching/imputation based on categorical variable

Description

Suitable donors are searched based on matching of the categorical variables. The variables are dropped in reversed order, so that the last element of 'match_var' is dropped first and the first element of the vector is dropped last.

Usage

matchImpute(
  data,
  variable = colnames(data)[!colnames(data) %in% match_var],
  match_var,
  imp_var = TRUE,
  imp_suffix = "imp"
)

Value

the imputed data set.

Arguments

data

data.frame, data.table or matrix

variable

variables to be imputed

match_var

variables used for matching

imp_var

TRUE/FALSE if a TRUE/FALSE variables for each imputed variable should be created show the imputation status

imp_suffix

suffix for the TRUE/FALSE variables showing the imputation status

Author

Johannes Gussenbauer, Alexander Kowarik

Details

The method works by sampling values from the suitable donors.

See Also

hotdeck()

Other imputation methods: hotdeck(), impPCA(), irmi(), kNN(), medianSamp(), rangerImpute(), regressionImp(), sampleCat()

Examples

data(sleep,package="VIM")
imp_data <- matchImpute(sleep,variable=c("NonD","Dream","Sleep","Span","Gest"),
  match_var=c("Exp","Danger"))

data(testdata,package="VIM")
imp_testdata1 <- matchImpute(testdata$wna,match_var=c("c1","c2","b1","b2"))

dt <- data.table::data.table(testdata$wna)
imp_testdata2 <- matchImpute(dt,match_var=c("c1","c2","b1","b2"))