Bayesian Information Criterion
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.
|
Description
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*log-likelihood + npar*log(nobs), where
npar represents the
number of parameters and nobs the number of
observations in the fitted model.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.Author(s)
Jose Pinheiro and Douglas BatesReferences
Schwarz, G. (1978) "Estimating the Dimension of a Model", Annals of
Statistics, 6, 461-464.See Also
logLik
, AIC
, BIC.logLik
Examples
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)