Extracts the Akaike information criterion (AIC) and the corrected AIC (AICc) from fitted models of formal
class glimML and possibly computes derived statistics.
Usage
## S3 method for class 'glimML':
AIC(object, \dots, k = 2)
Arguments
object
fitted model of formal class glimML (functions betabin or negbin).
...
optional list of fitted models separated by commas.
k
numeric scalar, with a default value set to 2, thus providing the regular AIC.
Details
$AIC = -2~\mbox{log-likelihood} + 2*n_{par}$, where $n_{par}$
represents the number of parameters in the fitted model.
$AICc = AIC + 2 * n_{par} * (n_{par} + 1) / (n_{obs} - n_{par} + 1)$,
where $n_{obs}$ is the number of observations used to compute the log-likelihood. It should be used when the number
of fitted parameters is large compared to sample size, i.e., when $n_{obs} / n_{par} < 40$ (Hurvich and Tsai, 1995).
References
Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference: a practical
information-theoretic approach. New-York, Springer-Verlag, 496 p.
Hurvich, C.M., Tsai, C.-L., 1995. Model selection for extended quasi-likelihood models in small samples.
Biometrics, 51 (3): 1077-1084.