Missing Value Analysis — BIFIE.mva • BIFIEsurvey

Conducts a missing value analysis.

BIFIE.mva( BIFIEobj, missvars, covariates=NULL, se=TRUE )

# S3 method for BIFIE.mva
summary(object,digits=4,...)

Arguments

BIFIEobj: Object of class BIFIEdata
missvars: Vector of variables for which missing value statistics should be computed
covariates: Vector of variables which work as covariates
se: Optional logical indicating whether statistical inference based on replication should be employed.
object: Object of class BIFIE.correl
digits: Number of digits for rounding output
...: Further arguments to be passed

Value

A list with following entries

stat.mva: Data frame with missing value statistics
res_list: List with extensive output split according to each variable in missvars
...: More values

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
BIFIEdata <- BIFIEsurvey::BIFIE.data( data.list=data.timss1,
                wgt=data.timss1[[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ] )

# missing value analysis for "scsci" and "books" and three covariates
res1 <- BIFIEsurvey::BIFIE.mva( BIFIEdata, missvars=c("scsci", "books" ),
             covariates=c("ASMMAT", "female", "ASSSCI") )
summary(res1)

# missing value analysis without statistical inference and without covariates
res2 <- BIFIEsurvey::BIFIE.mva( BIFIEdata, missvars=c("scsci", "books"), se=FALSE)
summary(res2)