Akaike's An Information Criterion for a gamBiCop Object

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).

# S4 method for 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.