# BIC

From lme4 v0.999375-37
by Doug

##### Bayesian Information Criterion

The `BIC`

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

##### 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 0.999375-37, License: GPL (>= 2)*

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