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%\docType{genericFunction}
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\alias{BIC,logLik-method}
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\title{Bayesian Information Criterion}
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The \code{\link[nlme]{BIC}} generic function calculates the Bayesian
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information criterion, also known as Schwarz's Bayesian criterion
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(SBC), for one or several fitted model objects for which a
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log-likelihood value can be obtained, according to the formula \eqn{-2
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\mbox{log-likelihood} + n_{par} \log(n_{obs})}{-2*log-likelihood +
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npar*log(nobs)}, where \eqn{n_{par}}{npar} represents the number of
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parameters and \eqn{n_{obs}}{nobs} the number of observations in the
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% \item{object}{An object of a suitable class for the BIC to be
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% calculated - usually a \code{\link[stats]{logLik}} object
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% created by a call to the \code{\link[stats]{logLik}} generic.
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% \item{\dots}{Some methods for this generic function may take
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% additional, optional arguments. At present none do.}
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if just one object is provided, returns a numeric value with the
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corresponding BIC; if more than one object are provided, returns a
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\code{data.frame} with rows corresponding to the objects and columns
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representing the number of parameters in the model (\code{df}) and the
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Estimating the Dimension of a Model,
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\emph{Annals of Statistics} \bold{6}, 461--464.
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\seealso{\code{\link[nlme]{BIC}}, \code{\link[stats]{logLik}},
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\code{\link[stats]{AIC}}}
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man/BIC.Rd
