miceadds (version 3.1-37)

mice.impute.eap: Imputation of a Variable with a Known Posterior Distribution

Description

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

Usage

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

# 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, method=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,
            method=impmethod, predictorMatrix=pM,  allow.na=TRUE, eap=eap)
summary(imp2)    # summary
# }