miceadds (version 2.2-0)

complete.miceadds: Creates Imputed Dataset from a mids.nmi or mids.1chain Object

Description

Creates imputed dataset from a mids.nmi or mids.1chain object.

Usage

complete.mids.nmi( x , action = c(1,1) )
complete.mids.1chain( x , action = 1 )

Arguments

x

Object of class mids.nmi (for complete.mids.nmi) or mids.1chain (for complete.mids.1chain)

action

A vector of length two indicating to indices of between and within imputed dataset for for complete.mids.nmi and an integer for the index of imputed dataset for complete.mids.1chain.

See Also

See also the corresponding mice::complete function and mitml::mitmlComplete. Imputation methods: mice.nmi, mice.1chain

Examples

## Not run: 
# #############################################################################
# # EXAMPLE 1: Nested multiple imputation and dataset extraction for TIMSS data
# #############################################################################
# 
# library(BIFIEsurvey)
# data(data.timss2 , package="BIFIEsurvey" )
# datlist <- data.timss2		
# 
# # remove first four variables
# M <- length(datlist)
# for (ll in 1:M){
#     datlist[[ll]] <- datlist[[ll]][ , -c(1:4) ]
#                 }
#                 
# #***************
# # (1) nested multiple imputation using mice
# imp1 <- mice.nmi( datlist ,  m=4 , maxit=3 )
# summary(imp1)
# 
# #***************
# # (2) nested multiple imputation using mice.1chain
# imp2 <- mice.nmi( datlist , Nimp=4 , burnin=10 ,iter=22, type="mice.1chain")
# summary(imp2)
# 
# #**************
# # extract dataset for third orginal dataset the second within imputation
# dat32a <- complete.mids.nmi( imp1 , action = c(3,2) )
# dat32b <- complete.mids.nmi( imp2 , action = c(3,2) )
# 
# #############################################################################
# # EXAMPLE 2: Imputation from one chain and extracting dataset for nhanes data
# #############################################################################
# 
# library(mice)
# data(nhanes, package="mice")
# 
# # nhanes data in one chain
# imp1 <- mice.1chain( nhanes , burnin=5 , iter=40 , Nimp=4 ,
#           imputationMethod=rep("norm" , 4 ) )
#     
# # extract first imputed dataset
# dati1 <- complete.mids.1chain( imp1 , action=1 )
# ## End(Not run)