BIFIE.univar {BIFIEsurvey}

Univariate Descriptive Statistics (Means and Standard Deviations)

Description

Computes some univariate descriptive statistics (means and standard deviations).

Usage

BIFIE.univar(BIFIEobj, vars, group=NULL, group_values=NULL, se=TRUE)

## S3 method for class 'BIFIE.univar'
summary(object,digits=3,...)

## S3 method for class 'BIFIE.univar'
coef(object,...)

## S3 method for class 'BIFIE.univar'
vcov(object,...)

Arguments

BIFIEobj

Object of class BIFIEdata

vars

Vector of variables for which statistics should be computed

group

Optional grouping variable(s)

group_values

Optional vector of grouping values. This can be omitted and grouping values will be determined automatically.

se

Optional logical indicating whether statistical inference based on replication should be employed.

object

Object of class BIFIE.univar

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

stat

Data frame with univariate statistics

stat_M

Data frame with means

stat_SD

Data frame with standard deviations

output

Extensive output with all replicated statistics

...

More values

See Also

See BIFIE.univar.test for a test of equal means and effect sizes \eta and d.

Descriptive statistics without statistical inference can be estimated by the collection of miceadds::ma.wtd.statNA functions from the miceadds package.

Further descriptive functions:

survey::svymean, intsvy::timss.mean, intsvy::timss.mean.pv, stats::weighted.mean, Hmisc::wtd.mean, miceadds::ma.wtd.meanNA

survey::svyvar, Hmisc::wtd.var, miceadds::ma.wtd.sdNA, miceadds::ma.wtd.covNA

Examples

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

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

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

# compute descriptives for plausible values
res1 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI","books") )
summary(res1)

# split descriptives by number of books
res2 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI"), group="books",
            group_values=1:5)
summary(res2)

#############################################################################
# EXAMPLE 2: TIMSS dataset with missings
#############################################################################

data(data.timss2)
data(data.timssrep)

# use first dataset with missing data from data.timss2
bdat1 <- BIFIEsurvey::BIFIE.data( data.list=data.timss2[[1]], wgt=data.timss2[[1]]$TOTWGT,
               wgtrep=data.timssrep[, -1 ])

# some descriptive statistics without statistical inference
res1a <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI","books"), se=FALSE)
# descriptive statistics with statistical inference
res1b <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI","books") )
summary(res1a)
summary(res1b)

# split descriptives by number of books
res2 <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI"), group="books")
# Note that if group_values is not specified as an argument it will be
# automatically determined by the observed frequencies in the dataset
summary(res2)