lme4 (version 0.999375-37)

BIC: Bayesian Information Criterion


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.



  • 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.


Schwarz, G. (1978) Estimating the Dimension of a Model, Annals of Statistics 6, 461--464.

See Also

BIC, logLik, AIC