BIC {nlme}R Documentation

Bayesian Information Criterion

Description

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, ...)

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 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 with rows corresponding to the objects and columns representing the number of parameters in the model (df) and the BIC.

Author(s)

Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu

References

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

See Also

logLik, AIC, BIC.logLik

Examples

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

[Package nlme version 3.1-80 Index]