subset_datlist: Subsetting Multiply Imputed Datasets and Nested Multiply...

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

Description

Returns a subsets of multiply imputed datasets or nested multiply imputed datasets. These function allows choosing parts of the imputed datasets using the index argument for multiply imputed datasets and index_between and index_within for nested multiply imputed datasets as well as the application of the base::subset S3 method for selecting cases and variables in datasets.

Usage

subset_datlist(datlist, subset = TRUE, select = NULL, expr_subset = NULL ,
        index = NULL, toclass = "datlist")

## S3 method for class 'datlist'
subset(x, subset, select = NULL, expr_subset = NULL , 
                     index = NULL , ...)
## S3 method for class 'imputationList'
subset(x, subset, select = NULL, expr_subset = NULL ,
                     index = NULL , ...)
## S3 method for class 'mids'
subset(x, subset, select = NULL, expr_subset = NULL, 
                     index = NULL , ...)
## S3 method for class 'mids.1chain'
subset(x, subset, select = NULL, expr_subset = NULL ,
                     index = NULL , ...)
                     
subset_nested.datlist( datlist , subset = TRUE, select = NULL , expr_subset = NULL, 
	  index_between = NULL , index_within = NULL , toclass = "nested.datlist" ,
          simplify = FALSE )
         
## S3 method for class 'nested.datlist'
subset(x, subset , select = NULL , expr_subset = NULL, 
                index_between = NULL, index_within = NULL, simplify=FALSE , ...)
## S3 method for class 'NestedImputationList'
subset(x, subset , select = NULL , expr_subset = NULL,
                index_between = NULL, index_within = NULL, simplify=FALSE , ...)

Arguments

datlist	For subset_datlist it is a list of datasets or an object of class datlist, imputationList, mids or mids.1chain. For subset_nested.datlist it is a list of datasets or an object of class nested.datlist or NestedImputationList.
subset	Logical expression indicating elements or rows to keep, see base::subset. subset can also be a numeric vector containing row indices.
select	Expression indicating columns to select from a data frame
expr_subset	Expression indicating a selection criterion for selection rows.
index	Vector of indices indicating which of the multiply imputed datasets should be selected.
toclass	The object class in which the datasets should be saved.
index_between	Index for between nest datasets
index_within	Index for within nest datasets
simplify	Optional logical indicating whether a nested multiply imputed dataset should be simplified to a multiplied imputed dataset.
x	Object containing multiply imputed or nested multiply imputed datasets
...	Further arguments to be passed.

Value

For multiply imputed datasets: Object of class datlist, imputationList or mids
For nested multiply imputed datasets: Object of class nested.datlist or NestedImputationList.

Note

If subsetting is applied to objects of class mids (or mids.1chain), then informations about the imputation procedure are lost.

Author(s)

Alexander Robitzsch

See Also

base::subset

Examples

#############################################################################
# EXAMPLE 1: Subsetting and selection of multiply imputed datasets
#############################################################################

data(data.ma02)

# define original list of datasets
datlist1a <- data.ma02
# object of class datlist
datlist1b <- miceadds::datlist_create(datlist1a)
datlist1b
# object of class imputationList
datlist1c <- mitools::imputationList(datlist1a)
datlist1c
# object of class mids
datlist1d <- miceadds::datlist2mids(datlist1a)
datlist1d

# select some imputed datasets
datlist2a <- miceadds::subset_datlist( datlist1a , index = c(5,3,7) )
datlist2a
# convert to class imputationList
datlist2b <- miceadds::subset_datlist( datlist1a , index = c(5,3,7), 
                      toclass = "imputationList")
datlist2b
# convert to class mids
datlist2c <- miceadds::subset_datlist( datlist1a , index = 1:3 , toclass = "mids")
datlist2c

## Not run: 
# select some variables
datlist3a <- miceadds::subset_datlist( datlist1a , select = c("idstud" , "books")  )
datlist3a
# Because datlist1b is a datlist it is equivalent to 
datlist3b <- subset( datlist1b , select = c("idstud" , "books")  )
datlist3b
# operating on imputationList class
datlist3c <- miceadds::subset_datlist( datlist1c , select = c("idstud" , "books")  )
datlist3c
# operating on mids class
datlist3d <- miceadds::subset_datlist( datlist1d , select = c("idstud" , "books")  )
datlist3d
# selection of rows and columns in multiply imputed datasets
datlist4a <- miceadds::subset_datlist( datlist1a , index = 1:5 , 
                  subset = datlist1a[[1]]$idschool < 1067 , 
                  select = c("idstud" , "idschool","hisei") )
datlist4a
# convert to class mids
datlist4b <- miceadds::subset_datlist( datlist1a , index = 1:5 , 
                  subset = datlist1a[[1]]$idschool < 1067 , 
                  select = c("idstud" , "idschool","hisei") , toclass = "mids" )
datlist4b
# The same functionality, but now applying to object of class mids datlist1d
datlist4c <- subset( datlist1d , index = 1:5 , subset = datlist1a[[1]]$idschool < 1067, 
                     select = c("idstud" , "idschool","hisei")  )
datlist4c

# expression for selecting rows specific in each data frame
# which can result in differently sized datasets (because the variable
# migrant is imputed)
datlist5a <- miceadds::subset_datlist( datlist1a ,  expr_subset = migrant == 1 )
datlist5a

# select the first 100 cases
datlist6a <- miceadds::subset_datlist( datlist1a , select = c("idstud" , "books"), 
                       subset=1:100 )
datlist6a

#############################################################################
# EXAMPLE 2: Subsetting and selection of nested multiply imputed datasets
#############################################################################

library(BIFIEsurvey)
data(data.timss4, package="BIFIEsurvey")
dat <- data.timss4

# create object of class 'nested.datlist'
datlist1a <- miceadds::nested.datlist_create( dat )
# create object of class 'NestedImputationList'
datlist1b <- miceadds::NestedImputationList(dat)
# create object of class 'mids.nmi'
datlist1c <- miceadds::datlist2mids(dat)

# select some between datasets
datlist2a <- subset_nested.datlist( datlist1a , index_between = c(1,3,4) )
datlist2a
# shorter version
datlist2b <- subset( datlist1a , index_between = c(1,3,4) )
datlist2b
# conversion of a NestedImputationList
datlist2c <- subset( datlist1b , index_between = c(1,3,4))
datlist2c
# select rows and columns
sel_cases <- datlist1a[[1]][[1]]$JKZONE <= 42
datlist3a <- subset( datlist1a , subset = sel_cases , 
                 select = c("IDSTUD","books", "ASMMAT") )
datlist3a
# remove within nest
datlist4a <- subset( datlist1a , index_within = 1 )
datlist4a
# remove within nest and simplify structure
datlist4b <- subset( datlist1a , index_within = 1 , simplify = TRUE)
datlist4b
datlist4c <- subset( datlist1b , index_within = 1 , simplify = TRUE)
datlist4c
# remove between nest
datlist5a <- subset( datlist1a , index_between = 1 , simplify = TRUE)
datlist5a
datlist5b <- subset( datlist1b , index_between = 1 , simplify = TRUE)
datlist5b

## End(Not run)

miceadds documentation built on June 20, 2017, 9:10 a.m.

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