miceadds (version 1.6-0)

scale_datlist: Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasets

Description

Adds a standardized variable to a list of multiply imputed datasets or a single dataset. This function extends base::scale for a data frame to a list of multiply imputed datasets.

Usage

scale_datlist(datlist, orig_var, trafo_var, weights = NULL, M = 0, SD = 1, 
    digits = NULL)

Arguments

datlist

A data frame, a list of multiply imputed datasets of one of the classes datlist or imputationList or a list of nested multiply imputed datasets of one of the classes nested_datlist or NestedImputationList

orig_var

Name of the variable to be transformed

trafo_var

Name of the standardized variable

weights

Optional vector of sample weights

M

Mean of the transformed variable

SD

Standard deviation of the transformed variable

digits

Number of digits used for rounding the standardized variable

Value

• A vector or a matrix

See Also

base::scale, ma.scale2

Examples

Run this code

#############################################################################
# EXAMPLE 1: Standardized variables in list of multiply imputed datasets
#############################################################################
	
data(data.ma02)
datlist <- data.ma02

#--- object of class 'datlist'
datlist <- datlist_create( datlist )

# mean and SD of variable hisei
ma.wtd.meanNA(data=datlist, weights = datlist[[1]]$studwgt , vars = "hisei" )
mean( unlist( lapply( datlist , FUN = function(data){
        weighted.mean( data$hisei , data$studwgt )  } ) ) )
ma.wtd.sdNA(data=datlist, weights = datlist[[1]]$studwgt , vars = "hisei" )
mean( unlist( lapply( datlist , FUN = function(data){
        sqrt( Hmisc::wtd.var( data$hisei , data$studwgt ) ) } ) ) )

# standardize variable hisei to M=100 and SD=15        
datlist1a <- scale_datlist( datlist=datlist, orig_var="hisei", trafo_var="hisei100", 
               weights = datlist[[1]]$studwgt , M=100, SD=15 )

# check mean and SD
ma.wtd.meanNA(data=datlist1a, weights = datlist[[1]]$studwgt , vars = "hisei100" )
ma.wtd.sdNA(data=datlist1a, weights = datlist[[1]]$studwgt , vars = "hisei100" )

#--- transformation for a single dataset
dat0 <- datlist[[1]]
dat0a <- scale_datlist( datlist=dat0 , orig_var="hisei" , trafo_var="hisei100" , 
                    weights = dat0$studwgt , M=100, SD=15 )
weighted.mean( dat0a[,"hisei"] ,  w = dat0a$studwgt )                    
weighted.mean( dat0a[,"hisei100"] ,  w = dat0a$studwgt )
sqrt( Hmisc::wtd.var( dat0a[,"hisei100"] ,  weights = dat0a$studwgt ) )

#--- Standardizations for objects of class imputationList
datlist2 <- mitools::imputationList(datlist)   # object class conversion
datlist2a <- scale_datlist( datlist=datlist2 , orig_var="hisei" , trafo_var="hisei100" , 
               weights = datlist[[1]]$studwgt , M=100, SD=15 )
               
#############################################################################
# EXAMPLE 2: Standardized variables in list of nested multiply imputed datasets
#############################################################################
               
# nested multiply imputed dataset in BIFIEsurvey package
data(data.timss4, package="BIFIEsurvey")
datlist <- data.timss4
wgt <- datlist[[1]][[1]]$TOTWGT

# class nested.datlist
imp1 <- nested.datlist_create( datlist )
# class NestedImputationList
imp2 <- NestedImputationList( datlist )

# standardize variable scsci
imp1a <- scale_datlist( datlist=imp1, orig_var="scsci", trafo_var="zscsci", weights =wgt)
# check descriptives
ma.wtd.meanNA( imp1a , weights=wgt , vars = c("scsci" , "zscsci" ) )
ma.wtd.sdNA( imp1a , weights=wgt , vars = c("scsci" , "zscsci" ) )

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