miceadds (version 2.2-0)

mice.impute.bygroup: Groupwise Imputation Function

Description

This function 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 )
# ## End(Not run)