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

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

[Package

*nlme* version 3.1-57

Index]