BIFIEsurvey (version 3.5-19)

BIFIE.waldtest: Wald Tests for BIFIE Methods

Description

This function performs a Wald test for objects of classes BIFIE.by, BIFIE.correl, BIFIE.crosstab, BIFIE.freq, BIFIE.linreg, BIFIE.logistreg and BIFIE.univar.

Usage

BIFIE.waldtest(BIFIE.method, Cdes, rdes, type=NULL)
# S3 method for BIFIE.waldtest
summary(object,digits=4,...)

Value

A list with following entries

stat.D

Data frame with \(D_1\) and \(D_2\) statistic, degrees of freedom and p value

...

More values

Arguments

BIFIE.method

Object of classes BIFIE.by, BIFIE.correl, BIFIE.crosstab, BIFIE.freq, BIFIE.linreg, BIFIE.logistreg or BIFIE.univar (see parnames in the Output of these methods for saved parameters)

Cdes

Design matrix \(C\) (see Details)

rdes

Design vector \(r\) (see Details)

type

Only applies to BIFIE.correl. In case of type="cov" covariances instead of correlations are used for parameter tests.

object

Object of class BIFIE.waldtest

digits

Number of digits for rounding output

...

Further arguments to be passed

Details

The Wald test is conducted for a parameter vector \(\bold{\theta}\), specifying the hypothesis \(C \bold{\theta}=r\). Statistical inference is performed by using the \(D_1\) and the \(D_2\) statistic (Enders, 2010, Ch. 8).

For objects of class bifie.univar, only hypotheses with respect to means are implemented.

References

Enders, C. K. (2010). Applied missing data analysis. Guilford Press.

See Also

survey::regTermTest, survey::anova.svyglm, car::linearHypothesis

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 ] )

#******************
#*** Model 1: Linear regression
res1 <- BIFIEsurvey::BIFIE.linreg( bdat, dep="ASMMAT", pre=c("one","books","migrant"),
         group="female" )
summary(res1)

#*** Wald test which tests whether sigma and R^2 values are the same
res1$parnames    # parameter names
pn <- res1$parnames ; PN <- length(pn)
Cdes <- matrix(0,nrow=2, ncol=PN)
colnames(Cdes) <- pn
# equality of R^2  ( R^2(female0) - R^2(female1)=0 )
Cdes[ 1, c("R^2_NA_female_0", "R^2_NA_female_1" ) ] <- c(1,-1)
# equality of sigma ( sigma(female0) - sigma(female1)=0)
Cdes[ 2, c("sigma_NA_female_0", "sigma_NA_female_1" ) ] <- c(1,-1)
# design vector
rdes <- rep(0,2)
# perform Wald test
wmod1 <- BIFIEsurvey::BIFIE.waldtest( BIFIE.method=res1, Cdes=Cdes, rdes=rdes )
summary(wmod1)

if (FALSE) {
#******************
#*** Model 2: Correlations

# compute some correlations
res2a <- BIFIEsurvey::BIFIE.correl( bdat, vars=c("ASMMAT","ASSSCI","migrant","books"))
summary(res2a)

# test whether r(MAT,migr)=r(SCI,migr) and r(MAT,books)=r(SCI,books)
pn <- res2a$parnames; PN <- length(pn)
Cdes <- matrix( 0, nrow=2, ncol=PN )
colnames(Cdes) <- pn
Cdes[ 1, c("ASMMAT_migrant", "ASSSCI_migrant") ] <- c(1,-1)
Cdes[ 2, c("ASMMAT_books", "ASSSCI_books") ] <- c(1,-1)
rdes <- rep(0,2)
# perform Wald test
wres2a <- BIFIEsurvey::BIFIE.waldtest( res2a, Cdes, rdes )
summary(wres2a)

#******************
#*** Model 3: Frequencies

# Number of books splitted by gender
res3a <- BIFIEsurvey::BIFIE.freq( bdat, vars=c("books"), group="female" )
summary(res3a)

# test whether book(cat4,female0)+book(cat5,female0)=book(cat4,female1)+book(cat5,female5)
pn <- res3a$parnames
PN <- length(pn)
Cdes <- matrix( 0, nrow=1, ncol=PN )
colnames(Cdes) <- pn
Cdes[ 1, c("books_4_female_0", "books_5_female_0",
    "books_4_female_1", "books_5_female_1" ) ] <- c(1,1,-1,-1)
rdes <- c(0)
# Wald test
wres3a <- BIFIEsurvey::BIFIE.waldtest( res3a, Cdes, rdes )
summary(wres3a)

#******************
#*** Model 4: Means

# math and science score splitted by gender
res4a <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI"), group="female")
summary(res4a)

# test whether there are significant gender differences in math and science
#=> multivariate ANOVA
pn <- res4a$parnames
PN <- length(pn)
Cdes <- matrix( 0, nrow=2, ncol=PN )
colnames(Cdes) <- pn
Cdes[ 1, c("ASMMAT_female_0", "ASMMAT_female_1"  ) ] <- c(1,-1)
Cdes[ 2, c("ASSSCI_female_0", "ASSSCI_female_1"  ) ] <- c(1,-1)
rdes <- rep(0,2)
# Wald test
wres4a <- BIFIEsurvey::BIFIE.waldtest( res4a, Cdes, rdes )
summary(wres4a)
}