\Header{BIC}{Bayesian Information Criterion}
\alias{BIC,ANY-method}{BIC}
\alias{BIC,logLik-method}{BIC}
\keyword{models}{BIC}
\begin{Description}\relax
This generic function calculates the Bayesian information criterion,
also known as Schwarz's Bayesian criterion (SBC), for one or several
fitted model objects for which a log-likelihood value can be obtained,
according to the formula \eqn{-2 \mbox{log-likelihood} + n_{par}
\log(n_{obs})}{-2*log-likelihood + npar*log(nobs)}, where
\eqn{n_{par}}{npar}  represents the
number of parameters and \eqn{n_{obs}}{nobs} the number of
observations in the fitted model.
\end{Description}
\begin{Usage}
\begin{verbatim}
BIC(object, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] An object of a suitable class for the BIC to be
calculated - usually a \code{"logLik"} object or an object for which
a \code{\Link{logLik}} method exists.

\item[\code{...}] Some methods for this generic function may take
additional, optional arguments.  At present none do.
\end{ldescription}
\end{Arguments}
\begin{Value}
Returns a numeric value with the corresponding BIC.
\end{Value}
\begin{References}\relax
Schwarz, G. (1978) "Estimating the Dimension of a Model", Annals of
Statistics, 6, 461-464.
\end{References}
\begin{SeeAlso}\relax
\code{\Link{logLik-methods}}, \code{\Link{AIC-methods}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
data(swiss)
lm1 <- lm(Fertility ~ . , data = swiss)
AIC(lm1)
BIC(lm1)


\end{ExampleCode}
\end{Examples}

