mice.impute.bygroup: Groupwise Imputation Function

In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Groupwise Imputation Function

Description

The function mice.impute.bygroup performs groupwise imputation for arbitrary imputation methods defined in mice.

Usage

mice.impute.bygroup(y, ry, x, wy=NULL, group, imputationFunction, ...)

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.
wy	Vector of length(y) indicating which entries should be imputed.
group	Name of grouping variable
imputationFunction	Imputation method for mice
...	More arguments to be passed to imputation function

Value

Vector of imputed values

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Cluster-specific imputation for some variables
#############################################################################

library(mice)
data( data.ma01, package="miceadds")
dat <- data.ma01

# use sub-dataset
dat <- dat[ dat$idschool <=1006, ]
V <- ncol(dat)
# create initial predictor matrix and imputation methods
predictorMatrix <- matrix( 1, nrow=V, ncol=V)
diag(predictorMatrix) <- 0
rownames(predictorMatrix) <- colnames(predictorMatrix) <- colnames(dat)
predictorMatrix[, c("idstud", "studwgt","urban" ) ] <- 0
method <- rep("norm", V)
names(method) <- colnames(dat)

#** groupwise imputation of variable books
method["books"] <- "bygroup"
# specify name of the grouping variable ('idschool') and imputation method ('norm')
group <- list( "books"="idschool" )
imputationFunction <- list("books"="norm" )

#** conduct multiple imputation in mice
imp <- mice::mice( dat, method=method, predictorMatrix=predictorMatrix,
            m=1, maxit=1, group=group, imputationFunction=imputationFunction )

#############################################################################
# EXAMPLE 2: Group-wise multilevel imputation '2l.pan'
#############################################################################

library(mice)
data( data.ma01, package="miceadds" )
dat <- data.ma01

# select data
dat <- dat[, c("idschool","hisei","books","female") ]
V <- ncol(dat)
dat <- dat[ ! is.na( dat$books), ]
# define factor variable

dat$books <- as.factor(dat$books)
# create initial predictor matrix and imputation methods
predictorMatrix <- matrix( 0, nrow=V, ncol=V)
rownames(predictorMatrix) <- colnames(predictorMatrix) <- colnames(dat)
predictorMatrix["idschool", ] <- 0
predictorMatrix[ "hisei", "idschool" ] <- -2
predictorMatrix[ "hisei", c("books","female") ] <- 1
method <- rep("", V)
names(method) <- colnames(dat)
method["hisei"] <- "bygroup"
group <- list( "hisei"="female" )
imputationFunction <- list("hisei"="2l.pan" )

#** conduct multiple imputation in mice
imp <- mice::mice( dat, method=method, predictorMatrix=predictorMatrix,
            m=1, maxit=1, group=group, imputationFunction=imputationFunction )
str(imp)

## End(Not run)

miceadds documentation built on May 29, 2024, 11:05 a.m.