| BIC {nlme} | R Documentation |
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 Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu
Schwarz, G. (1978) "Estimating the Dimension of a Model", Annals of Statistics, 6, 461-464.
fm1 <- lm(distance ~ age, data = Orthodont) # no random effects BIC(fm1) fm2 <- lme(distance ~ age, data = Orthodont) # random is ~age BIC(fm1, fm2)