Second-order Akaike Information Criterion

Calculate Second-order Akaike Information Criterion for one or several fitted model objects (AIC\(_{c}\), AIC for small samples).

AICc(object, ..., k = 2, REML = NULL)

Arguments

object: a fitted model object for which there exists a logLik method, or a "logLik" object.
...: optionally more fitted model objects.
k: the ‘penalty’ per parameter to be used; the default k = 2 is the classical AIC.
REML: optional logical value, passed to the logLik method indicating whether the restricted log-likelihood or log-likelihood should be used. The default is to use the method used for model estimation.

Value

If just one object is provided, returns a numeric value with the corresponding AIC\(_{c}\); if more than one object are provided, returns a data.frame with rows corresponding to the objects and columns representing the number of parameters in the model (df) and AIC\(_{c}\).

Note

AIC\(_{c}\) should be used instead AIC when sample size is small in comparison to the number of estimated parameters (Burnham & Anderson 2002 recommend its use when \(n / K < 40\)).

References

Burnham, K. P. and Anderson, D. R. 2002 Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York, Springer-Verlag.

Hurvich, C. M. and Tsai, C.-L. 1989 Regression and time series model selection in small samples, Biometrika 76, 297–307.

Author

Kamil Bartoń