mice.impute.hotDeck.Rd
Imputes a variable under a random draw from a pool of donors defined by a distance function. Uncertainty with respect to the creation of donor pools is introduced by drawing a Bootstrap sample (approximate Bayesian Bootstrap, ABB) from observations with complete data (see Andridge & Little, 2010).
mice.impute.hotDeck(y, ry, x, donors=5, method="Mahalanobis", ...)
y | Incomplete data vector of length |
---|---|
ry | Vector of missing data pattern ( |
x | Matrix ( |
donors | Number of donors used for random sampling of nearest neighbors in imputation |
method | Method used for computation of weights in distance function.
Options are the Mahalanobis metric ( |
... | Further arguments to be passed |
A vector of length nmis=sum(!ry)
with imputed values.
Andridge, R. R., & and Little, R. J. A. (2010). A review of hot deck imputation for survey non-response. International Statistical Review, 78(1), 40-64. doi: 10.1111/j.1751-5823.2010.00103.x
See also the packages hot.deck and HotDeckImputation.
if (FALSE) { ############################################################################# # EXAMPLE 1: Hot deck imputation NHANES dataset ############################################################################# data(nhanes, package="mice") dat <- nhanes #*** prepare imputation method vars <- colnames(dat) V <- length(vars) impMethod <- rep("hotDeck", V) method <- "cor" #*** imputation in mice imp <- mice::mice( data=as.matrix(dat), m=1, method=impMethod, method=method ) summary(imp) }