miceadds (version 1.5-0)

with.miceadds: 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::within.data.frame, mitools::with.imputationList.

Usage

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

## S3 method for class 'mids.nmi':
with(data, expr, ...)

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

## S3 method for class 'NestedImputationList':
with(data, expr, fun , ...)

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

## 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.

Value

• with.mids.1chain: List of class mira. with.mids.nmi: List of class mira.nmi. within.imputationList: List of class imputationList. with.NestedImputationList: List of class NestedImputationResultList. within.NestedImputationList: List of class NestedImputationList.

See Also

mice::with.mids, mice::summary.mira, base::within.data.frame, mitools::with.imputationList Imputation functions in miceadds: mice.1chain, mice.nmi

Examples

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

# calculate some descriptive statistics
res2 <- with( imp , expr = c( mean(hyp) , sd(age) ) )
res2

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

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

#############################################################################
# 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 <- 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 , lm( ASMMAT ~ migrant + female ) )	
summary(res1)

# convert mids.nmi object into an object of class NestedImputationList
datlist1 <- mids2datlist( imp1 )
datlist1 <- NestedImputationList( datlist1 )

# use with function
res1b <- with( datlist1 , 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
                    } )