nlme (version 3.1-31)

BIC: 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 $-2 \mbox{log-likelihood} + n_{par} \log(n_{obs})$, where $n_{par}$ represents the number of parameters and $n_{obs}$ the number of observations in the fitted model.

Usage

BIC(object, ...)

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.

See Also

logLik, AIC, BIC.logLik

Examples

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