Description Usage Arguments Details Value Warning References See Also Examples

Performs likelihood-ratio tests on nested models. Currently, one method was implemented
for beta-binomial models (`betabin`

) or negative-binomial models (`negbin`

).

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`object` |
Fitted model of class “glimML”. |

`...` |
Further models to be tested or arguments passed to the |

The `anova`

method for models of formal class “glimML” needs at least 2 nested models of the
same type (either beta-binomial or negative-binomial models: they cannot be mixed). The quantity of interest is
the deviance difference between the compared models: it is a log-likelihood ratio statistic. Under the null
hypothesis that 2 nested models fit the data equally well, the deviance difference has an approximate
*chi-squared* distribution with degrees of freedom = the difference in the number of parameters between
the compared models (Mc Cullagh and Nelder, 1989).

An object of formal class “anova.glimML” with 3 slots:

`models` |
A vector of character strings with each component giving the name of the models and the formulas for the fixed and random effects. | |||||||||||||||||||||||||||||||||

`anova.table` |
A data frame containing the results. Row names correspond to the models.
| |||||||||||||||||||||||||||||||||

`type` |
A character chain indicating the kind of fitted model: “BB” for beta-binomial, or “NB” for negative-binomial model. |

The comparison between 2 or more models will only be valid if they are fitted to the same data set.

McCullagh, P., Nelder, J.A., 1989. *Generalized linear models*. London, Chapman & Hall, 511 p.

See Appendix C. Likelihood ratio statistics, p. 476-478.

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