Summarizing iterative bias reduction fits
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).
## S3 method for class 'ibr' AIC(object, ..., k = 2)
object |
A fitted model object of class ibr. |
... |
Not used. |
k |
Numeric, the penalty per parameter to be used; the
default |
The ibr method for AIC, AIC.ibr() calculates
log(sigma^2)+2*df/n, where df is the trace
of the smoother.
returns a numeric value
with the corresponding AIC (or BIC, or ..., depending on k).
Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.
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 .
## 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)
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