Information criteria on a tulpa fit

AIC and BIC penalise a maximised log-likelihood. logLik.tulpa_fit() reports one only for a fit that maximised over every parameter it reports (quantity = "log_likelihood", as agq_fit() does); a mean log posterior, a log evidence and a conditional log marginal likelihood are not, and both criteria refuse them rather than return a number. Compare those fits by their evidence, or by compare_models(criterion = "waic") / "loo".

Usage

# S3 method for class 'tulpa_fit'
AIC(object, ..., k = 2)

# S3 method for class 'tulpa_fit'
BIC(object, ...)

Arguments

object

A tulpa_fit object.

...

Further fits.

k

Penalty per parameter.

Value

As stats::AIC() / stats::BIC() for maximised log-likelihoods; errors otherwise.