Groupwise Imputation Function

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

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

if (FALSE) {
#############################################################################
# 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)
}