miceadds (version 2.2-0)

mice.impute.weighted.pmm: Imputation by Weighted Predictive Mean Matching

Description

Imputation by predictive mean matching using sampling weights.

Usage

mice.impute.weighted.pmm(y, ry, x, imputationWeights = NULL,  pls.facs = 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.

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.

...

Further arguments to be passed

Value

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

See Also

For imputation with the linear normal regression and sampling weights see mice.impute.weighted.norm.

Examples

## Not run: 
# #############################################################################
# # 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 , m=0 , 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 , imputationMethod=meth , 
#            imputationWeights= dat[,"studwgt"] , m=3 , maxit=5)
# ## End(Not run)