AIC.ibr: Summarizing iterative bias reduction fits

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Generic function calculating the Akaike information criterion for one model objects of ibr class for which a log-likelihood value can be obtained, according to the formula -2 log(sigma^2) + k df/n, where df represents the effective degree of freedom (trace) of the smoother in the fitted model, and k = 2 for the usual AIC, or k = \log(n) (n the number of observations) for the so-called BIC or SBC (Schwarz's Bayesian criterion).

Usage

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## S3 method for class 'ibr'
AIC(object, ..., k = 2)

Arguments

object

A fitted model object of class ibr.

...

Not used.

k

Numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

Details

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

Value

returns a numeric value with the corresponding AIC (or BIC, or ..., depending on k).

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

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## Not run: data(ozone, package = "ibr")
res.ibr <- ibr(ozone[,-1],ozone[,1],df=1.2)
summary(res.ibr)
predict(res.ibr)
## End(Not run)

ibr documentation built on May 2, 2019, 8:22 a.m.