AICc: Corrected Akaike Information Criterion

Description Usage Arguments Value Author(s) References See Also

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

1
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 21, 2017, 12:22 a.m.
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