Corrected Akaike Information Criterion
This generic function calculates the small-sample corrected Akaike information criterion, for one or several fitted model objects for which a log-likelihood value can be obtained, according to the formula -2*log-likelihood + 2*npar*(nobs/(nobs-npar-2)), where npar represents the number of parameters and nobs the number of observations in the fitted model.
An object of a suitable class for the AICc to be
calculated - usually a
Some methods for this generic function may take additional, optional arguments. At present none do.
Returns a numeric value with the corresponding AICc.
Hurvich, C.M. and Tsai, C.L. (1989) "Regression and Time Series Model Selection in Small Samples", Biometrika, 76, 297-307.