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

    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)

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