BIC {nlme}R Documentation

Bayesian Information Criterion


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.


BIC(object, ...)


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.


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.


Jose Pinheiro and Douglas Bates


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

See Also

logLik, AIC, BIC.logLik


fm1 <- lm(distance ~ age, data = Orthodont) # no random effects
fm2 <- lme(distance ~ age, data = Orthodont) # random is ~age
BIC(fm1, fm2)

[Package nlme version 3.1-57 Index]