miceadds (version 2.2-0)

mice.nmi: Nested Multiple Imputation

Description

Performs nested multiple imputation (Rubin, 2003) for the functions mice::mice and mice.1chain. The function mice.nmi generates an object of class mids.nmi.

Usage

mice.nmi(datlist, type = "mice", ...)
"summary"(object, ...)
"print"(x, ...)

Arguments

datlist

List of datasets for which nested multiple imputation should be applied

type

Imputation model: type="mice" for mice::mice or type="mice.1chain" for mice.1chain.

...

Arguments to be passed to mice::mice or mice.1chain.

object

Object of class mids.nmi.

x

Object of class mids.nmi.

Value

Object of class mids.nmi with entries

References

Rubin, D. B. (2003). Nested multiple imputation of NMES via partially incompatible MCMC. Statistica Neerlandica, 57(1), 3-18.

See Also

For imputation models see mice::mice and mice.1chain.

Functions for analyses of nested multiply imputed datasets: complete.mids.nmi, with.mids.nmi, pool.mids.nmi

Examples

## Not run: 	
# #############################################################################
# # EXAMPLE 1: Nested multiple imputation for TIMSS data
# #############################################################################
# 
# library(BIFIEsurvey)
# data(data.timss2 , package="BIFIEsurvey" )
# datlist <- data.timss2		
#    # list of 5 datasets containing 5 plausible values
# 
# #** define imputation method and predictor matrix
# data <- datlist[[1]]
# V <- ncol(data)
# # variables
# vars <- colnames(data)
# # variables not used for imputation
# vars_unused <- scan.vec("IDSTUD TOTWGT  JKZONE  JKREP" )
# 
# #- define imputation method
# impMethod <- rep("norm" , V )
# names(impMethod) <- vars
# impMethod[ vars_unused ] <- ""
# 
# #- define predictor matrix
# predM <- matrix( 1 , V , V )
# colnames(predM) <- rownames(predM) <- vars
# diag(predM) <- 0
# predM[ , vars_unused ] <- 0
# 
# #***************
# # (1) nested multiple imputation using mice
# imp1 <- mice.nmi( datlist , imputationMethod=impMethod , predictorMatrix=predM, 
#                 m=4 , maxit=3 )
# summary(imp1)
# 
# #***************
# # (2) nested multiple imputation using mice.1chain
# imp2 <- mice.nmi( datlist , imputationMethod=impMethod , predictorMatrix=predM , 
#             Nimp=4 , burnin=10 ,iter =22, type="mice.1chain")
# summary(imp2)
# ## End(Not run)