BIFIEsurvey (version 3.3-12)

BIFIE.mva: Missing Value Analysis

Description

Conducts a missing value analysis.

Usage

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

# NOT RUN {
#############################################################################
# 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)
# }