AIC.gamBiCop {gamCopula}

Akaike's An Information Criterion for a gamBiCop Object

Description

Function calculating Akaike's 'An Information Criterion' (AIC) for an object of the class gamBiCop (note that the models are usually fitted by penalized likelihood maximization).

Usage

## S4 method for signature 'gamBiCop'
AIC(object, ..., k = 2)

Arguments

object

An object of the class gamBiCop.

...

un-used in this class

k

numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

Value

A numeric value with the corresponding AIC.

See Also

AIC and BIC.