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
"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.
Value
returns a numeric value
with the corresponding AIC (or BIC, or ..., depending on k).
Details
The ibr method for AIC, AIC.ibr() calculates
$log(sigma^2)+2*df/n$, where df is the trace
of the smoother.
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 .