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.
USAGE:
BIC(object, ...)
REQUIRED 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 ARGUMENTS:
...
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 or
bdFrame 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.
SEE ALSO:
,
,
EXAMPLES:
fm1 <- lm(distance ~ age, data = Orthodont) # no random effects
fm2 <- lme(distance ~ age, data = Orthodont) # random is ~age
BIC(fm1, fm2)