Likelihood ratio tests of model terms in scale and nominal formulae
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 |
scope |
a formula or character vector specifying the terms to add to scale
or nominal. In In In |
trace |
if |
... |
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.
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