NestedImputationList.Rd
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
.
NestedImputationList( datasets ) # S3 method for NestedImputationList print(x, ...) # S3 method for NestedImputationResultList MIcombine(results, ...)
datasets | List of lists of datasets which are created by nested multiple imputation. |
---|---|
x | Object of class |
results | Object of class |
... | Further arguments to be passed. |
Function NestedImputationList
: Object of class NestedImputationList
.
Function MIcombine.NestedImputationList
:
Object of class mipo.nmi
.
if (FALSE) { ############################################################################# # 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) }