AIC.cmp: Akaike's Information Criterion

In thomas-fung/mpcmp: Mean-Parametrized Conway-Maxwell Poisson (COM-Poisson) Regression

Akaike's Information Criterion

Description

A function calculating Akaike's Information Criterion (AIC) based on the log-likelihood value extracted from logLik.cmp, according to the formula -2log-likelihood + knpar, where npar represents the number of parameters in the fitted model, and k=2 for the usual AIC or k=log(n) (n being the number of observations) for the so-called BIC (Bayesian Information Criterion).

Usage

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

Arguments

object	an object class 'cmp' object, obtained from a call to glm.cmp
...	other arguments passed to or from other methods (currently unused).
k	numeric: the penalty per parameter to be used; the default k = 2 is the classical AIC.

Details

When comparing models fitted by maximum likelihood to the same data, the smaller the AIC or BIC, the better the fit.

Value

A numeric value with the corresponding AIC (or BIC, or ..., depends on k).

See Also

logLik.cmp, nobs.cmp, glm.cmp

thomas-fung/mpcmp documentation built on June 13, 2022, 6:20 p.m.