Calculates the Akaike Information Criterion for objects of class oglmx. Calculate using the formula \(-2*loglikelihood + k*npar\) where \(npar\) represents the number of parameters in the model and \(k\) is the cost of additional parameters, equal to 2 for the AIC, it is \(k=\log(n)\) with \(n\) the number of observations for the BIC.
# S3 method for oglmx
AIC(object, ..., k = 2)object of class oglmx
additional arguments. Currently ignored.
the penalty per parameter to be used.
A numeric value with the AIC.
When comparing models by maximium likelihood estimation the smaller the value of the AIC the better.
AIC.