BIC.mmlcr {mmlcr}R Documentation

Bayesian Information Criterion

Description

This generic function calculates the Bayesian information criterion, also known as Schwarz's Bayesian criterion (SBC), for an mmlcr object 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

## S3 method for class 'mmlcr':
BIC(object, ...)

Arguments

object a fitted mmlcr object.
... 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

AIC, mmlcrObject

Examples

## Not run: data(mmlcrdf)
## Not run: 
mmlcrdf.mmlcr2 <- mmlcr(outer =  ~ sex + cov1 | id, 
components = list(
        list(formula = resp1 ~ 1, class = "cnormonce", min = 0, max = 50),
        list(formula = resp2 ~ poly(age, 2) + tcov1, class = "poislong"),
        list(formula = resp3 ~ poly(age, 2), class = "multinomlong")
), data = mmlcrdf, n.groups = 2)
## End(Not run)

## Not run: BIC(mmlcrdf.mmlcr2)

[Package mmlcr version 1.3.5 Index]