miceadds (version 2.10-14)

mice.impute.bygroup: 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, 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.

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
#############################################################################	
	
data( data.ma01 )
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
imputationMethod <- rep("norm" , V)
names(imputationMethod) <- colnames(dat)

#** groupwise imputation of variable books 
imputationMethod["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, imputationMethod=imputationMethod, predictorMatrix=predictorMatrix,
            m=1 , maxit=1 , group = group , imputationFunction = imputationFunction )
# }