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 $-2 \mbox{log-likelihood} + n_{par}
\log(n_{obs})$, where
$n_{par}$ represents the
number of parameters and $n_{obs}$ the number of
observations in the fitted model.
Usage
BIC(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.
Value
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 corresponding to the objects and columns
representing the number of parameters in the model (df) and the
BIC.
References
Schwarz, G. (1978) Estimating the Dimension of a Model.
Annals of Statistics, 6, 461-464.
fm1 <- lm(distance ~ age, data = Orthodont) # no random effectsBIC(fm1)
BIC(logLik(fm1))fm2 <- lme(distance ~ age, data = Orthodont) # random is ~ageBIC(fm1, fm2)