[R-sig-ME] calculation of AIC
maj at stats.waikato.ac.nz
Mon Feb 2 01:31:08 CET 2009
Although if log-likelihood is arbitrary up to an additive constant we
may add a sufficiently large constant onto any two AICs and push the
first non-equal digit of the two AICs as far down as we want!
John Maindonald wrote:
> On 02/02/2009, at 11:06 AM, Jeremiah Rounds wrote:
>>> Date: Sun, 1 Feb 2009 11:41:44 -0800> From: adik at ilovebacon.org> To:
>>> orzack at freshpond.org> CC: r-sig-mixed-models at r-project.org> Subject:
>>> Re: [R-sig-ME] calculation of AIC> > > On Sun, 1 Feb 2009, orzack
>>> wrote:> > > Speaking of this, does anybody know how to change the
>>> default rounding for > > glm (and lmer) OR for an R session in
>>> general (e.g., so that a regular call > > to glm would generate AIC
>>> values with more digits)?> > > 1/7>  0.1428571> >
>>> options(digits=22)> > 1/7>  0.1428571428571428> >
>>> options(digits=23)> Error in options(digits = 23) :> invalid 'digits'
>>> parameter, allowed 1...22> > options(digits=22)> > ...but this of
>>> course won't help if there is explicit rounding programmed> into
>>> glm/lmer. I also do not understand what would motivate this code,>
>>> instead of a more straightforward round(aic,0).> > --Adam
>> First, glm apparently is not using rounding from "round". glm is
>> using signif or equivalent logic. There is a difference. The
>> difference is significant digits is a fairly precisely defined
>> notion. It is the number of digits you keep on the front of the power
>> of 10 in "a X 10^b". Round is much more cosmetic from what I can tell.
>> Second, round(aic,0) is not more straightforward in the presence of
>> very small AIC. Here the distinct difference from round(a,0) and
>> signif(a,4) is that you never know prior to viewing the aic how many
>> digits round(a,0) will be keeping from the original unrounded AIC
>> value. With signif(a,4) you always know there will be a 4 digit
>> number times 10 to some power.
>> Third, in my limited statistical experience (I am a master's student)
>> AIC is not a method where those extra digits ever really matter. I
>> did a project on AIC/BIC model selection. IMO in order to use AIC
>> properly you have to consider the models in a nearby neighborhood to
>> the best AIC as just as good as the model with the best AIC and
>> consider sensitivity in your estimates. There is no real context
>> where you can properly say "the fifth significant digit of AIC has
>> decided that model A is better than model B, and so I discard model B."
> Well said!
> John Maindonald email: john.maindonald at anu.edu.au
> phone : +61 2 (6125)3473 fax : +61 2(6125)5549
> Centre for Mathematics & Its Applications, Room 1194,
> John Dedman Mathematical Sciences Building (Building 27)
> Australian National University, Canberra ACT 0200.
>> That is what I think,
>>>> R-sig-mixed-models at r-project.org mailing list>
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Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: maj at waikato.ac.nz Fax 7 838 4155
Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 1395 862
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