miceadds (version 2.2-0)

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::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

"with"(data, expr, ...)
"with"(data, expr, fun , ...)
"with"(data, expr, ...)
"with"(data, expr, fun , ...)
"with"(data, expr, fun , ...)
"within"(data, expr, ...)
"within"(data, expr, ...)
"within"(data, expr, ...)
"within"(data, expr, ...)
withPool_MI(x, ...)
withPool_NMI(x, ...)
"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

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

#############################################################################
# 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 = 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 <- 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 <- mids2datlist( imp )
datlist <- mitools::imputationList(datlist)

## Not run: 
# # 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$imputations[[1]] )
# 
# # convert into a datlist
# datlist2b <- datlist_create( datlist2 )
# 
# # apply linear model using expression
# mod1a <- with( datlist2 , expr = stats::lm( hyp ~ age.D3 ) )
# # do the same but now with a function argument
# mod1b <- with( datlist2 , fun = function(data){
#                     stats::lm( data$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 <- 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 <- mids2datlist( imp1 )
# datlist1 <- NestedImputationList( datlist1 )
# # convert into nested.datlist object
# datlist1b <- 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)