miceadds (version 2.2-0)

mice.impute.hotDeck: Imputation of a Variable Using Probabilistic Hot Deck Imputation

Description

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).

Usage

mice.impute.hotDeck(y, ry, x, donors=5, method = "Mahalanobis" , ...)

Arguments

y

Incomplete data vector of length n

ry

Vector of missing data pattern (FALSE -- missing, TRUE -- observed)

x

Matrix (n x p) of complete covariates.

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 (method="Mahalanobis"), weighted by correlations of covariates with the outcome (method="cor") and weighting by linear regression coefficients (method="lm").

...

Further arguments to be passed

Value

A vector of length nmis=sum(!ry) with imputed values.

References

Andridge, R. R., & and Little, R. J. A. (2010). A review of hot deck imputation for survey non-response. International Statistical Review, 78, 40-64.

See Also

See also the packages hot.deck and HotDeckImputation.

Examples

## Not run: 
# #############################################################################
# # 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, imputationMethod=impMethod,
#                method=method )
# summary(imp)
# ## End(Not run)