BIFIE.data.transform {BIFIEsurvey}R Documentation

Data Transformation for BIFIEdata Objects

Description

Computes a data transformation for BIFIEdata objects.

Usage

BIFIE.data.transform( bifieobj, transform.formula, varnames.new=NULL )

Arguments

bifieobj

Object of class BIFIEdata

transform.formula

R formula object for data transformation.

varnames.new

Optional vector of names for new defined variables.

Value

An object of class BIFIEdata. Additional values are

varnames.added

Added variables in data transformation

varsindex.added

Indices of added variables

Examples

library(miceadds)

#############################################################################
# EXAMPLE 1: Data transformations for TIMSS data
#############################################################################

data(data.timss2)
data(data.timssrep)
# create BIFIEdata object
bifieobj1 <- BIFIEsurvey::BIFIE.data( data.timss2, wgt=data.timss2[[1]]$TOTWGT,
            wgtrep=data.timssrep[,-1] )
# create BIFIEdata object in compact way (cdata=TRUE)
bifieobj2 <- BIFIEsurvey::BIFIE.data( data.timss2, wgt=data.timss2[[1]]$TOTWGT,
            wgtrep=data.timssrep[,-1], cdata=TRUE)

#****************************
#*** Transformation 1: Squared and cubic book variable
transform.formula <- ~ I( books^2 ) + I( books^3 )
# as.character(transform.formula)
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj1,
                  transform.formula=transform.formula)
bifieobj$variables
# rename added variables
bifieobj$varnames[ bifieobj$varsindex.added ] <- c("books_sq", "books_cub")

# check descriptive statistics
res1 <- BIFIEsurvey::BIFIE.univar( bifieobj, vars=c("books_sq", "books_cub" ) )
summary(res1)

## Not run: 
#****************************
#*** Transformation 2: Create dummy variables for variable book
transform.formula <- ~ as.factor(books)
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                    transform.formula=transform.formula )
##   Included 5 variables: as.factor(books)1 as.factor(books)2 as.factor(books)3
##        as.factor(books)4 as.factor(books)5
bifieobj$varnames[ bifieobj$varsindex.added ] <- paste0("books_D", 1:5)

#****************************
#*** Transformation 3: Discretized mathematics score
hi3a <- BIFIEsurvey::BIFIE.hist( bifieobj, vars="ASMMAT" )
plot(hi3a)
transform.formula <- ~ I( as.numeric(cut( ASMMAT, breaks=seq(200,800,100) )) )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                 transform.formula=transform.formula, varnames.new="ASMMAT_discret")
hi3b <- BIFIEsurvey::BIFIE.hist( bifieobj, vars="ASMMAT_discret", breaks=1:7 )
plot(hi3b)
# check frequencies
fr3b <- BIFIEsurvey::BIFIE.freq( bifieobj, vars="ASMMAT_discret", se=FALSE )
summary(fr3b)

#****************************
#*** Transformation 4: include standardization variables for book variable

# start with testing the transformation function on a single dataset
dat1 <- bifieobj$dat1
stats::weighted.mean( dat1[,"books"], dat1[,"TOTWGT"], na.rm=TRUE)
sqrt( Hmisc::wtd.var( dat1[,"books"], dat1[,"TOTWGT"], na.rm=TRUE) )
# z standardization
transform.formula <- ~ I( ( books - weighted.mean( books, TOTWGT, na.rm=TRUE) )/
                                sqrt( Hmisc::wtd.var( books, TOTWGT, na.rm=TRUE) ))
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
               transform.formula=transform.formula, varnames.new="z_books" )
# standardize variable books with M=500 and SD=100
transform.formula <- ~ I(
        500 + 100*( books - stats::weighted.mean( books, w=TOTWGT, na.rm=TRUE) ) /
              sqrt( Hmisc::wtd.var( books, weights=TOTWGT, na.rm=TRUE) )  )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
             transform.formula=transform.formula, varnames.new="z500_books" )

# standardize variable books with respect to M and SD of ALL imputed datasets
res <- BIFIEsurvey::BIFIE.univar( bifieobj, vars="books" )
summary(res)
##       var  Nweight Ncases     M M_SE M_fmi M_VarMI M_VarRep    SD SD_SE SD_fmi
##   1 books 76588.72   4554 2.945 0.04     0       0    0.002 1.146 0.015      0
M <- round(res$output$mean1,5)
SD <- round(res$output$sd1,5)
transform.formula <- paste0( " ~ I( ( books - ",  M, " ) / ", SD, ")"  )
##   > transform.formula
##   [1] " ~ I( ( books - 2.94496 ) / 1.14609)"
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                 transform.formula=stats::as.formula(transform.formula),
                 varnames.new="zall_books" )

# check statistics
res4 <- BIFIEsurvey::BIFIE.univar( bifieobj,
              vars=c("z_books", "z500_books", "zall_books") )
summary(res4)

#****************************
#*** Transformation 5: include rank transformation for variable ASMMAT

# calculate percentage ranks using wtd.rank function from Hmisc package
dat1 <- bifieobj$dat1
100 * Hmisc::wtd.rank( dat1[,"ASMMAT"], w=dat1[,"TOTWGT"] ) / sum( dat1[,"TOTWGT"] )
# define an auxiliary function for calculating percentage ranks
wtd.percrank <- function( x, w ){
    100 * Hmisc::wtd.rank( x, w, na.rm=TRUE ) / sum( w, na.rm=TRUE )
}
wtd.percrank( dat1[,"ASMMAT"], dat1[,"TOTWGT"] )
# define transformation formula
transform.formula <- ~ I( wtd.percrank( ASMMAT, TOTWGT ) )
# add ranks to BIFIEdata object
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
               transform.formula=transform.formula,  varnames.new="ASMMAT_rk")
# check statistic
res5 <- BIFIEsurvey::BIFIE.univar( bifieobj, vars=c("ASMMAT_rk" ) )
summary(res5)

#****************************
#*** Transformation 6: recode variable books

library(car)
# recode variable books according to "1,2=0, 3,4=1, 5=2"
dat1 <- bifieobj$dat1
# use Recode function from car package
car::Recode( dat1[,"books"], "1:2='0'; c(3,4)='1';5='2'")
# define transformation formula
transform.formula <- ~ I( car::Recode( books, "1:2='0'; c(3,4)='1';5='2'") )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
               transform.formula=transform.formula,  varnames.new="book_rec" )
res6 <- BIFIEsurvey::BIFIE.freq( bifieobj, vars=c("book_rec" ) )
summary(res6)

#****************************
#*** Transformation 7: include some variables aggregated to the school level

dat1 <- as.data.frame(bifieobj$dat1)
# at first, create school ID in the dataset by transforming the student ID
dat1$idschool <- as.numeric(substring( dat1$IDSTUD, 1, 5 ))
transform.formula <- ~ I( as.numeric( substring( IDSTUD, 1, 5 ) ) )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
               transform.formula=transform.formula, varnames.new="idschool" )

#*** test function for a single dataset bifieobj$dat1
dat1 <- as.data.frame(bifieobj$dat1)
gm <- miceadds::GroupMean( data=dat1$ASMMAT, group=dat1$idschool, extend=TRUE)[,2]

# add school mean ASMMAT
tformula <- ~ I( miceadds::GroupMean( ASMMAT, group=idschool, extend=TRUE)[,2] )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj, transform.formula=tformula,
                varnames.new="M_ASMMAT" )
# add within group centered mathematics values of ASMMAT
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                transform.formula=~ 0 + I( ASMMAT - M_ASMMAT ),
                varnames.new="WC_ASMMAT" )

# add school mean books
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                transform.formula=~ 0 + I( add.groupmean( books, idschool ) ),
                varnames.new="M_books" )

#****************************
#*** Transformation 8: include fitted values and residuals from a linear model
# create new BIFIEdata object
data(data.timss1)
bifieobj3 <- BIFIEsurvey::BIFIE.data( data.timss1, wgt=data.timss1[[1]]$TOTWGT,
            wgtrep=data.timssrep[,-1] )

# specify transformation
transform.formula <- ~ I( fitted( stats::lm( ASMMAT ~ migrant + female ) ) ) +
                             I( residuals( stats::lm( ASMMAT ~ migrant + female ) ) )
  # Note that lm omits cases in regression by listwise deletion.
# add fitted values and residual to BIFIEdata object
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj3,
                  transform.formula=transform.formula )
bifieobj$varnames[ bifieobj$varsindex.added ] <- c("math_fitted1", "math_resid1")

#****************************
#*** Transformation 9: Including principal component scores in BIFIEdata object

# define auxiliary function for extracting PCA scores
BIFIE.princomp <- function( formula, Ncomp ){
    X <- stats::princomp( formula, cor=TRUE)
    Xp <- X$scores[, 1:Ncomp ]
    return(Xp)
}
# define transformation formula
transform.formula <- ~ I( BIFIE.princomp( ~ migrant + female + books + lang + ASMMAT, 3 ))
# apply transformation
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj3,
                transform.formula=transform.formula )
bifieobj$varnames[ bifieobj$varsindex.added ] <- c("pca_sc1", "pca_sc2","pca_sc3")
# check descriptive statistics
res9 <- BIFIEsurvey::BIFIE.univar( bifieobj, vars="pca_sc1", se=FALSE)
summary(res9)
res9$output$mean1M

# The transformation formula can also be conveniently generated by string operations
vars <- c("migrant", "female", "books", "lang" )
transform.formula2 <- as.formula( paste0( "~ 0 + I ( BIFIE.princomp( ~ ",
       paste0( vars, collapse="+" ),  ", 3 ) )") )
  ##   > transform.formula2
  ##   ~ I(BIFIE.princomp(~migrant + female + books + lang, 3))

#****************************
#*** Transformation 10: Overwriting variables books and migrant
bifieobj4 <-  BIFIEsurvey::BIFIE.data.transform( bifieobj3,
                  transform.formula=~ I( 1*(books >=1 ) ) + I(2*migrant),
                  varnames.new=c("books","migrant") )
summary(bifieobj4)

## End(Not run)

[Package BIFIEsurvey version 3.3-12 Index]