nominal.test: Likelihood ratio tests of model terms in scale and nominal...
In ordinal: Regression Models for Ordinal Data

Description Usage Arguments Details Value Author(s) Examples

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, nominal_test provides likelihood ratio tests of the proportional odds assumption. The scale_test tests can be given a similar interpretation.

nominal_test(object, ...)

## S3 method for class 'clm'
nominal_test(object, scope, trace=FALSE, ...)

scale_test(object, ...)

## S3 method for class 'clm'
scale_test(object, scope, trace=FALSE, ...)

`object`	for the `clm` method an object of class `"clm"`, i.e., the result of a call to `clm`.
`scope`	a formula or character vector specifying the terms to add to scale or nominal. In `nominal_test` terms in scope already in `nominal` are ignored. In `scale_test` terms in scope already in `scale` are ignored. In `nominal_test` the default is to add all terms from `formula` (location part) and `scale` that are not also in `nominal`. In `scale_test` the default is to add all terms from `formula` (location part) that are not also in `scale`.
`trace`	if `TRUE` additional information may be given on the fits as they are tried.
`...`	arguments passed to or from other methods.

The definition of AIC is only up to an additive constant because the likelihood function is only defined up to an additive constant.

A table of class "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.

Rune Haubo B Christensen

## 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.
}