mice.impute.eap: Imputation of a Variable with a Known Posterior Distribution In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Description

This function imputes values of a variable for which the mean and the standard deviation of the posterior distribution is known.

Usage

 `1` ```mice.impute.eap(y, ry, x, eap, ...) ```

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. `eap` List with means and standard deviations of the posterior distribution (see Examples). If for multiple variables posterior distributions are known, then it is a list named in which each list entry is named according th variable to be imputed and each list entry contains the variable's EAP and standard deviation of the EAP. `...` Further arguments to be passed

Value

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

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 28 29 30 31 32 33 34 35 36``` ```## Not run: ############################################################################# # EXAMPLE 1: Imputation based on known posterior distribution ############################################################################# data(data.ma03) dat <- data.ma03 # definiere variable 'math_PV' as the plausible value imputation of math dat\$math_PV <- NA vars <- colnames(dat) dat1 <- as.matrix( dat[,vars] ) # define imputation methods impmethod <- rep( "pmm", length(vars) ) names(impmethod) <- vars # define plausible value imputation based on EAP and SEEAP for 'math_PV' impmethod[ "math_PV" ] <- "eap" eap <- list( "math_PV"=list( "M"=dat\$math_EAP, "SE"=dat\$math_SEEAP ) ) # define predictor matrix pM <- 1 - diag(1,length(vars)) rownames(pM) <- colnames(pM) <- vars pM[,c("idstud","math_EAP", "math_SEEAP") ] <- 0 # remove some variables from imputation model # imputation using three parallel chains imp1 <- mice::mice( dat1, m=3, maxit=5, imputationMethod=impmethod, predictorMatrix=pM, allow.na=TRUE, eap=eap ) summary(imp1) # summary # imputation using one long chain imp2 <- miceadds::mice.1chain( dat1, burnin=10, iter=20, Nimp=3, imputationMethod=impmethod, predictorMatrix=pM, allow.na=TRUE, eap=eap) summary(imp2) # summary ## End(Not run) ```

miceadds documentation built on Dec. 11, 2018, 5:05 p.m.