BIC.ibr: Information Criterion for ibr

In ibr: Iterative Bias Reduction

Information Criterion for ibr

Description

Functions calculating the Bayesian Informative Criterion , the Generalized Cross Validation criterion and the Corrected Akaike information criterion.

Usage

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

## S3 method for class 'ibr'
GCV(object, ...)

## S3 method for class 'ibr'
AICc(object, ...)

Arguments

object	A fitted model object of class ibr.
...	Only for compatibility purpose with BIC of nlme package.

Details

The ibr method for BIC, BIC.ibr() calculates \log(sigma^2)+log(n)*df/n, where df is the trace of the smoother.

The ibr method for GCV, GCV.ibr() calculates \log(sigma^2)-2*\log(1-df/n)

The ibr method for AICc, AICc.ibr() calculates \log(sigma^2)+1+(2*(df+1))/(n-df-2).

Value

Returns a numeric value with the corresponding BIC, GCV or AICc.

Author(s)

Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.

References

Hurvich, C. M., Simonoff J. S. and Tsai, C. L. (1998) Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion. Journal of the Royal Statistical Society, Series B, 60, 271-293 .

See Also

ibr, summary.ibr

Examples

## Not run: data(ozone, package = "ibr")
res.ibr <- ibr(ozone[,-1],ozone[,1])
BIC(res.ibr)
GCV(res.ibr)
AICc(res.ibr)

## End(Not run)

ibr documentation built on Sept. 13, 2023, 5:08 p.m.