Description Usage Arguments Details Value Methods Note Examples

Various functions for likelihood-based and information-theoretic model selection of likelihood models

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`object` |
A |

`...` |
An optional list of additional |

`nobs` |
Number of observations (sometimes obtainable as an attribute of the fit or of the log-likelihood) |

`k` |
penalty parameter (nearly always left at its default value of 2) |

Further arguments to `BIC`

can be specified
in the `...`

list: `delta`

(logical)
specifies whether to include a column for delta-BIC
in the output.

A table of the BIC values, degrees of freedom, and possibly delta-BIC values relative to the minimum-BIC model

- logLik
`signature(object = "mle2")`

: Extract maximized log-likelihood.- AIC
`signature(object = "mle2")`

: Calculate Akaike Information Criterion- AICc
`signature(object = "mle2")`

: Calculate small-sample corrected Akaike Information Criterion

- anova
`signature(object="mle2")`

: Likelihood Ratio Test comparision of different models

This is implemented in an ugly way and could probably be improved!

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