AICc: Corrected Akaike Information Criterion

In mcplR: Multiple Cue Probability Learning in R

Description

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.

Usage

AICc(object, ...)

Arguments

object	An object of a suitable class for the AICc to be calculated - usually a "logLik" object or an object for which a logLik method exists.
...	Some methods for this generic function may take additional, optional arguments. At present none do.

Value

Returns a numeric value with the corresponding AICc.

Author(s)

Maarten Speekenbrink

References

Hurvich, C.M. and Tsai, C.L. (1989) "Regression and Time Series Model Selection in Small Samples", Biometrika, 76, 297-307.

See Also

logLik, AIC

mcplR documentation built on May 2, 2019, 4:42 p.m.