BIC

0th

Percentile

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

Keywords
models
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.

References

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

logLik, AIC, BIC.logLik

• BIC
• BIC.lm
• BIC.lmList
• BIC.gls
• BIC.lme
• BIC.nls
• BIC.nlsList
• BIC.nlme
Examples
library(nlme) 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)
Documentation reproduced from package nlme, version 3.1-1, License: GPL version 2 or later

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