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ibr (version 2.0-4)

BIC: 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 ibr
BIC(object, ...)

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

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

Value

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

Arguments

object

A fitted model object of class ibr.

...

Only for compatibility purpose with BIC of nlme package.

Author

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

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 \(\log(sigma^2)+1+(2*(df+1))/(n-df-2)\).

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

Run this code
if (FALSE) data(ozone, package = "ibr")
res.ibr <- ibr(ozone[,-1],ozone[,1])
BIC(res.ibr)
GCV(res.ibr)
AICc(res.ibr)

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