mice.impute.weighted.pmm.RdImputation 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.
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) }