Compute AIC and (approximate) DIC for evmOpt objects
# S3 method for evmOpt
AIC(object, penalized = FALSE, nsamp = 1000, DIC, WAIC, ..., k = 2)The AIC and DIC
fit model object
whether to use the penalized log-likelihood
Number of approximate Gaussian sample to use in computing DIC.
Defaults to nsamp=1e3. Only used when the object has class 'evmOpt'.
Logical. Whether to compute DIC. Defaults to DIC = TRUE.
Only applicable to objects of class 'evmSim'.
Logical. Whether to compute WAIC. Defaults to WAIC = TRUE.
Only applicable to objects of class 'evmSim'.
other arguments currently ignored
numeric, the penalty per parameter to be used; the
default k = 2 is the classical AIC.
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.