hotdeck: Hot-Deck Imputation

In VIM: Visualization and Imputation of Missing Values

Hot-Deck Imputation

Description

Implementation of the popular Sequential, Random (within a domain) hot-deck algorithm for imputation.

Usage

hotdeck(
  data,
  variable = NULL,
  ord_var = NULL,
  domain_var = NULL,
  makeNA = NULL,
  NAcond = NULL,
  impNA = TRUE,
  donorcond = NULL,
  imp_var = TRUE,
  imp_suffix = "imp"
)

Arguments

data	data.frame or matrix
variable	variables where missing values should be imputed (not overlapping with ord_var)
ord_var	variables for sorting the data set before imputation (not overlapping with variable)
domain_var	variables for building domains and impute within these domains
makeNA	list of length equal to the number of variables, with values, that should be converted to NA for each variable
NAcond	list of length equal to the number of variables, with a condition for imputing a NA
impNA	TRUE/FALSE whether NA should be imputed
donorcond	list of length equal to the number of variables, with a donorcond condition as character string. e.g. ">5" or c(">5","<10). If the list element for a variable is NULL no condition will be applied for this variable.
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.

Note

If the sequential hotdeck does not lead to a suitable, a random donor in the group will be used.

Author(s)

Alexander Kowarik

References

A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 1-16.

See Also

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

Examples

data(sleep)
sleepI <- hotdeck(sleep)
sleepI2 <- hotdeck(sleep,ord_var="BodyWgt",domain_var="Pred")

# Usage of donorcond in a simple example
sleepI3 <- hotdeck(
  sleep,
  variable = c("NonD", "Dream", "Sleep", "Span", "Gest"),
  ord_var = "BodyWgt", domain_var = "Pred",
  donorcond = list(">4", "<17", ">1.5", "%between%c(8,13)", ">5")
)

set.seed(132)
nRows <- 1e3
# Generate a data set with nRows rows and several variables
x <- data.frame(
  x = rnorm(nRows), y = rnorm(nRows),
  z = sample(LETTERS, nRows, replace = TRUE),
  d1 = sample(LETTERS[1:3], nRows, replace = TRUE),
  d2 = sample(LETTERS[1:2], nRows, replace = TRUE),
  o1 = rnorm(nRows), o2 = rnorm(nRows), o3 = rnorm(100)
)
origX <- x
x[sample(1:nRows,nRows/10), 1] <- NA
x[sample(1:nRows,nRows/10), 2] <- NA
x[sample(1:nRows,nRows/10), 3] <- NA
x[sample(1:nRows,nRows/10), 4] <- NA
xImp <- hotdeck(x,ord_var = c("o1", "o2", "o3"), domain_var = "d2")

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