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\name{nominal_test}
\alias{nominal_test}
\alias{scale_test}
\alias{nominal_test.clm}
\alias{scale_test.clm}
\title{
Likelihood ratio tests of model terms in scale and nominal formulae
}
\description{
Add all model terms to scale and nominal formulae and perform
likelihood ratio tests. These tests can be viewed as goodness-of-fit
tests. With the logit link, \code{nominal_test} provides likelihood
ratio tests of the proportional odds assumption. The \code{scale_test}
tests can be given a similar interpretation.
}
\usage{
nominal_test(object, ...)
\method{nominal_test}{clm}(object, scope, trace=FALSE, ...)
scale_test(object, ...)
\method{scale_test}{clm}(object, scope, trace=FALSE, ...)
}
\arguments{
\item{object}{for the \code{clm} method an object of class
\code{"clm"}, i.e., the result of a call to \code{clm}.
}
\item{scope}{
a formula or character vector specifying the terms to add to scale
or nominal. In \code{nominal_test} terms in scope already in
\code{nominal} are ignored. In \code{scale_test} terms in scope
already in \code{scale} are ignored.
In \code{nominal_test} the default is to add all terms
from \code{formula} (location part) and \code{scale} that are not
also in \code{nominal}.
In \code{scale_test} the default is to add
all terms from \code{formula} (location part) that are not also in
\code{scale}.
}
\item{trace}{
if \code{TRUE} additional information may be given on the fits as
they are tried.
}
\item{\dots}{
arguments passed to or from other methods.
}
}
\value{
A table of class \code{"anova"} containing columns for the change
in degrees of freedom, AIC, the likelihood ratio statistic and a
p-value based on the asymptotic chi-square distribtion of the
likelihood ratio statistic under the null hypothesis.
}
\details{
The definition of AIC is only up to an additive constant because the
likelihood function is only defined up to an additive constant.
}
\author{Rune Haubo B Christensen}
\examples{
## Fit cumulative link model:
fm <- clm(rating ~ temp + contact, data=wine)
summary(fm)
## test partial proportional odds assumption for temp and contact:
nominal_test(fm)
## no evidence of non-proportional odds.
## test if there are signs of scale effects:
scale_test(fm)
## no evidence of scale effects.
## tests of scale and nominal effects for the housing data from MASS:
if(require(MASS)) {
fm1 <- clm(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
scale_test(fm1)
nominal_test(fm1)
## Evidence of multiplicative/scale effect of 'Cont'. This is a breach
## of the proportional odds assumption.
}
}
\keyword{models}
|
