

   AAkkaaiikkee IInnffoorrmmaattiioonn CCrriitteerriioonn

        AIC(object, ...)

   AArrgguummeennttss::

     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.

   DDeessccrriippttiioonn::

        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.

   VVaalluuee::

        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 correspond-
        ing to the objects and columns representing the number
        of parameters in the model (`df') and the AIC.

   AAuutthhoorr((ss))::

        Jose Pinheiro and Douglas Bates

   RReeffeerreenncceess::

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

   SSeeee AAllssoo::

        `logLik', `BIC', `AIC.logLik'

   EExxaammpplleess::

        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)

