> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of B??kony Veronika
> Sent: 18 June 2005 14:00
> To: r-help at stat.math.ethz.ch
> Subject: [R] how 'stepAIC' selects?
>
>
> Dear all,
> Could anyone please tell me how 'step' or 'stepAIC' works?
Does it
> simply select the model with the smallest AIC from all the possible
> models? Or does it perform any test eg. whether the decrease
> in "information content" between a model with a given predictor
and
> another without it is "significant"?
> Thanks for help!
> VB
As a complement to what Uwe said, you may want to consider that Sakamoto
et al., in p. 84, Ch. 4, Remark 2, of "Akaike Information
Criterion Statistics", 1986, KTK Scientific Publishers, write "From
the relation
between AIC and the entropy, if the differences of AIC's for MODEL(j) and
MODEL(k) is larger than 1~2, then the difference is considered to be
significant". Unfortunately I could not find any further ellaboration of
this
corollary anywhere in the book, but maybe I didn't look hard enough.
Additionally,
you may want to work with "evidence ratios" and Akaike weights, as
recommended
in Burnham and Anderson, "Model Selection and Multimodel Inference",
2002,
Springer. I am under the impression that to try to interpret the AIC in terms of
sampling-distribution inference theory, such as in signficance tests, is to miss
the
point.
Ruben