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, ...)y |
Incomplete data vector of length |
ry |
Vector of missing data pattern ( |
x |
Matrix ( |
wy |
Logical vector of length |
imputationWeights |
Optional vector of sampling weights |
pls.facs |
Number of factors in PLS regression (if used). The default is |
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} |
A vector of length nmis=sum(!ry) with imputed values.
## Not run:
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# 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)
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