Akaike Information Criterion

Usage

AIC(object, ...)

Arguments

object a fitted model object, for which there exists a logLik method to extract the corresponding log-likelihood, or an object inheriting from class logLik.
... optional fitted model objects.

Description

This generic function calculates the 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, where npar represents the number of parameters in the fitted model. When comparing fitted objects, the smaller the AIC, the better the fit.

Value

if just one object is provided, returns a numeric value with the corresponding AIC; 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 the AIC.

Author(s)

Jose Pinheiro and Douglas Bates

References

Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986) "Akaike Information Criterion Statistics", D. Reidel Publishing Company.

See Also

logLik, BIC, AIC.logLik

Examples

library(lme)
data(Orthodont)
fm1 <- lm(distance ~ age, data = Orthodont) # no random effects
fm1 <- lme(distance ~ age, data = Orthodont) # random is ~age
AIC(fm1, fm2)

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