BIC

0th

Percentile

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

BIC, logLik, AIC

Aliases
  • BIC,logLik-method
  • BIC,ANY-method
Documentation reproduced from package lme4, version 0.999375-37, License: GPL (>= 2)

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