Fast matching/imputation based on categorical variable

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.

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

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

Value

the imputed data set.

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(), vimpute(), xgboostImpute()

Author

Johannes Gussenbauer, Alexander Kowarik

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"))