Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression

Imputation by predictive mean matching or normal linear regression using sampling weights.

mice.impute.weighted.pmm(y, ry, x, wy=NULL, imputationWeights=NULL,
      pls.facs=NULL, interactions=NULL, quadratics=NULL, donors=5, ...)

mice.impute.weighted.norm(y, ry, x, wy=NULL, ridge=1e-05, pls.facs=NULL,
     imputationWeights=NULL, interactions=NULL, quadratics=NULL, ...)

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.
wy	Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.
imputationWeights	Optional vector of sampling weights
pls.facs	Number of factors in PLS regression (if used). The default is NULL which means that no PLS regression is used for dimension reduction.
interactions	Optional vector of variables for which interactions should be created
quadratics	Optional vector of variables which should also be included as quadratic effects.
donors	Number of donors
...	Further arguments to be passed
ridge	Ridge parameter in the diagonal of \( \bold{X}'\bold{X}\)

Value

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

Examples

if (FALSE) {
#############################################################################
# EXAMPLE 1: Imputation using sample weights
#############################################################################

data( data.ma01)
set.seed(977)

# select subsample
dat <- as.matrix(data.ma01)
dat <- dat[ 1:1000, ]

# empty imputation
imp0 <- mice::mice( dat, maxit=0)

# redefine imputation methods
meth <- imp0$method
meth[ meth=="pmm"  ] <- "weighted.pmm"
meth[ c("paredu", "books", "migrant" ) ] <- "weighted.norm"
# redefine predictor matrix
pm <- imp0$predictorMatrix
pm[, 1:3 ] <- 0
# do imputation
imp <- mice::mice( dat, predictorMatrix=pm, method=meth,
           imputationWeights=dat[,"studwgt"], m=3, maxit=5)
}