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, ...) 

REQUIRED 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 ARGUMENTS:

...
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 or bdFrame 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:

, ,

EXAMPLES:

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