miceadds (version 2.10-14)

NestedImputationList: Functions for Analysis of Nested Multiply Imputed Datasets

Description

The function NestedImputationList takes a list of lists of datasets and converts this into an object of class NestedImputationList.

Statistical models can be estimated with the function with.NestedImputationList.

The mitools::MIcombine method can be used for objects of class NestedImputationResultList which are the output of with.NestedImputationList.

Usage

NestedImputationList( datasets )
# S3 method for NestedImputationList
print(x , ...)
# S3 method for NestedImputationResultList
MIcombine(results, ...)

Arguments

datasets

List of lists of datasets which are created by nested multiple imputation.

x

Object of class NestedImputationResultsList

results

Object of class NestedImputationResultsList

…

Further arguments to be passed.

Value

Function NestedImputationList: Object of class NestedImputationList.

Function MIcombine.NestedImputationList: Object of class mipo.nmi.

See Also

with.NestedImputationList, within.NestedImputationList, pool.mids.nmi, NMIcombine

Examples

# NOT RUN {
#############################################################################
# EXAMPLE 1: Nested multiple imputation and conversion into an object of class
#            NestedImputationList 
#############################################################################

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) ]
                }
                
# nested multiple imputation using mice
imp1 <- miceadds::mice.nmi( datlist ,  m=3 , maxit=2 )
summary(imp1)

# create object of class NestedImputationList
datlist1 <- miceadds::mids2datlist( imp1 )
datlist1 <- miceadds::NestedImputationList( datlist1 )

# estimate linear model using with
res1 <- with( datlist1 , stats::lm( ASMMAT ~ female + migrant ) )
# pool results
mres1 <- mitools::MIcombine( res1 )
summary(mres1)
coef(mres1)
vcov(mres1)
# }