# mice.impute.weighted.pmm: Imputation by Weighted Predictive Mean Matching or Weighted... In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

## Description

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

## Usage

 ```1 2 3 4 5``` ```mice.impute.weighted.pmm(y, ry, x, imputationWeights = NULL, pls.facs = NULL, interactions = NULL, quadratics = NULL, ...) mice.impute.weighted.norm(y, ry, x, 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. `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 `ridge` Ridge parameter in the diagonal of \bold{X}'\bold{X}

## Value

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

## Author(s)

Alexander Robitzsch

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```## 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) ```

miceadds documentation built on April 1, 2018, 12:26 p.m.