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.

1 2 3 4 5 6 7 8 9 | ```
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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## 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.
}
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.