# BIC

From lme4 v0.99875-2
by Douglas Bates

##### 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

##### 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

*Documentation reproduced from package lme4, version 0.99875-2, License: GPL version 2 or later*

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