# BIC

From nlme v3.1-1

##### 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.

##### See Also

##### 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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