Description Usage Arguments Details Value See Also

Compute AIC and (approximate) DIC for `evmOpt`

objects

1 2 3 |

`object` |
fit model object |

`penalized` |
whether to use the penalized log-likelihood |

`nsamp` |
Number of approximate Gaussian sample to use in computing DIC.
Defaults to |

`...` |
other arguments currently ignored |

`k` |
numeric, the penalty per parameter to be used; the
default |

If the object has class 'evmOpt', `nsamp`

random draws are
made from the Gaussian distribution with mean and covariance inferred from
the model object. The result will be an approximate DIC. Note that AIC should
not be trusted if priors are not flat. For example, if you use a regularizing
prior on xi, say xi ~ N(0, 0.25), AIC can be misleading and DIC should be
preferred. If the object has class 'evmSim', the actual posterior draws are
used in the computation. Also note that sometimes the optimizer returns
an approximatae covariance that is not postive-semidefinite, in which case
the DIC will be reported as NA.

The AIC and DIC

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