BIC: Bayesian Information Criterion
Description
The BIC 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.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.