matchImpute: Fast matching/imputation based on categorical variable

In VIM: Visualization and Imputation of Missing Values

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"
)

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

Details

The method works by sampling values from the suitable donors.

Value

the imputed data set.

Author(s)

Johannes Gussenbauer, Alexander Kowarik

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"))

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