with.miceadds: Evaluates an Expression for (Nested) Multiply Imputed...

In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Evaluates an Expression for (Nested) Multiply Imputed Datasets

Description

Evaluates an expression for (nested) multiply imputed datasets. These functions extend the following functions: mice::with.mids, base::with, base::within.data.frame, mitools::with.imputationList.

The withPool functions try to pool estimates (by simple averaging) obtained by with or a list of results of imputed datasets.

Usage

## S3 method for class 'mids.1chain'
with(data, expr, ...)
## S3 method for class 'datlist'
with(data, expr, fun, ...)

## S3 method for class 'mids.nmi'
with(data, expr, ...)
## S3 method for class 'nested.datlist'
with(data, expr, fun, ...)
## S3 method for class 'NestedImputationList'
with(data, expr, fun, ...)

## S3 method for class 'datlist'
within(data, expr, ...)
## S3 method for class 'imputationList'
within(data, expr, ...)

## S3 method for class 'nested.datlist'
within(data, expr, ...)
## S3 method for class 'NestedImputationList'
within(data, expr, ...)

withPool_MI(x, ...)

withPool_NMI(x, ...)

## S3 method for class 'mira.nmi'
summary(object, ...)

Arguments

data	Object of class mids.1chain, mids.nmi, imputationList or NestedImputationList
expr	Expression with a formula object.
fun	A function taking a data frame argument
...	Additional parameters to be passed to expr.
object	Object of class mira.nmi.
x	List with vectors or matrices as results of an analysis for (nested) multiply imputed datasets.

Value

with.mids.1chain: List of class mira.

with.mids.nmi: List of class mira.nmi.

with.datlist: List of class imputationResultList.

with.NestedImputationList or with.nested.datlist: List of class NestedImputationResultList.

within.imputationList: List of class imputationList.

within.NestedImputationList: List of class NestedImputationList.

withPool_MI or withPool_NMI: Vector or matrix with pooled estimates

Author(s)

Slightly modified code of mice::with.mids, mice::summary.mira, base::within.data.frame

See Also

See the corresponding functionality in base, mice, mitools and mitml packages:
mice::with.mids, mitools::with.imputationList, mitml::with.mitml.list, base::with

base::within.data.frame, mitml::within.mitml.list,

mice::summary.mira,

Imputation functions in miceadds: mice.1chain, mice.nmi

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: One chain nhanes data | application of 'with' and 'within'
#############################################################################

library(mice)
data(nhanes, package="mice")
set.seed(9090)

# nhanes data in one chain
imp <- miceadds::mice.1chain( nhanes, burnin=5, iter=40, Nimp=4 )
# apply linear regression
res <- with( imp, expr=stats::lm( hyp ~ age + bmi  ) )
summary(res)
# pool results
summary( mice::pool(res))

# calculate some descriptive statistics
res2 <- with( imp, expr=c("M1"=mean(hyp), "SD_age"=stats::sd(age) ) )
# pool estimates
withPool_MI(res2)

# with method for datlist
imp1 <- miceadds::datlist_create(imp)
res2b <- with( imp1, fun=function(data){
                    dfr <- data.frame("M"=colMeans(data),
                             "Q5"=apply( data, 2, stats::quantile, .05 ),
                             "Q95"=apply( data, 2, stats::quantile, .95 ) )
                    return(dfr)
                        } )
withPool_MI(res2b)

# convert mids object into an object of class imputationList
datlist <- miceadds::mids2datlist( imp )
datlist <- mitools::imputationList(datlist)

# define formulas for modification of the data frames in imputationList object
datlist2 <- within( datlist, {
                     age.D3 <- 1*(age==3)
                     hyp_chl <- hyp * chl
                        } )
# look at modified dataset
head( datlist2$imputations[[1]] )

# convert into a datlist
datlist2b <- miceadds::datlist_create( datlist2 )

# apply linear model using expression
mod1a <- with( datlist2, expr=stats::lm( hyp ~ age.D3 ) )
# do the same but now with a function argument
mod1b <- with( datlist2, fun=function(data){
                    stats::lm( data$hyp ~ data$age.D3 )
                        } )
# apply the same model for object datlist2b
mod2a <- with( datlist2b, expr=lm( hyp ~ age.D3 ) )
mod2b <- with( datlist2b, fun=function(data){
                    stats::lm( data$hyp ~ data$age.D3 )
                        } )

mitools::MIcombine(mod1a)
mitools::MIcombine(mod1b)
mitools::MIcombine(mod2a)
mitools::MIcombine(mod2b)

#############################################################################
# EXAMPLE 2: Nested multiple imputation and application of with/within methods
#############################################################################

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=4, maxit=3 )
summary(imp1)
# apply linear model and use summary method for all analyses of imputed datasets
res1 <- with( imp1, stats::lm( ASMMAT ~ migrant + female ) )
summary(res1)

# convert mids.nmi object into an object of class NestedImputationList
datlist1 <- miceadds::mids2datlist( imp1 )
datlist1 <- miceadds::NestedImputationList( datlist1 )
# convert into nested.datlist object
datlist1b <- miceadds::nested.datlist_create(datlist1)

# use with function
res1b <- with( datlist1, stats::glm( ASMMAT ~ migrant + female ) )
# apply for nested.datlist
res1c <- with( datlist1b, stats::glm( ASMMAT ~ migrant + female ) )

# use within function for data transformations
datlist2 <- within( datlist1, {
                highsc <- 1*(ASSSCI > 600)
                books_dum <- 1*(books>=3)
                rm(scsci)   # remove variable scsci
                    } )

# include random number in each dataset
N <- attr( datlist1b, "nobs")
datlist3 <- within( datlist1b, {
                rn <- stats::runif( N, 0, .5 )
                    } )

#-- some applications of withPool_NMI
# mean and SD
res3a <- with( imp1, c( "m1"=mean(ASMMAT), "sd1"=stats::sd(ASMMAT) ) )
withPool_NMI(res3a)
# quantiles
vars <- c("ASMMAT", "lang", "scsci")
res3b <- with( datlist1b, fun=function(data){
                dat <- data[,vars]
                res0 <- sapply( vars, FUN=function(vv){
                    stats::quantile( dat[,vv], probs=c(.25, .50, .75) )
                                    } )
                t(res0)
                    } )
withPool_NMI(res3b)

## End(Not run)

miceadds documentation built on May 29, 2024, 11:05 a.m.