

   BBaayyeessiiaann IInnffoorrmmaattiioonn CCrriitteerriioonn

        BIC(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 Bayesian informa-
        tion criterion, also known as Schwarz's Bayesian crite-
        rion (SBC), for one or several fitted model objects for
        which a log-likelihood value can be obtained, according
        to the formula -2*log-likelihood + npar*log(nobs),
        where npar  represents the number of parameters and
        nobs the number of observations in the fitted model.

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

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

   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::

        Schwarz, G. (1978) "Estimating the Dimension of a
        Model", Annals of Statistics, 6, 461-464.

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

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

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

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

