On Fri, 1 Sep 2006, [EMAIL PROTECTED] wrote:
> Prof Brian Ripley wrote
> > 3) As I recall, you were doing model selection via AIC on 20,000
> >observations. You might want to think hard about that, since AIC is
> >designed for good prediction. I would do model exploration on a much
>
Prof Brian Ripley wrote
> Probably not, but you have the ability to profile in R and find out.
Thanks. This is certainly something I could check, and I shall do so.
>
>
> Some more comments;
>
> 1) The Fortran code that underlies glm is that of lm.fit that only makes
>use of level-1 BLA
George,
Logistic regression with ONLY factors?
In principle this can be solved by casting this as a log-linear model of
counts and using iterative proportional fitting.
For sparse data like yours (i.e. a table with 2 counts and >= 2^31
cells), it will be necessary to use a method that doe
Please look at http://boinc.berkeley.edu/
Your problem seems to be similar to the ones for which BOINC is used. I am not
sure how to do this with R, though. May be other people in this can help.
Anupam.
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[EMAIL PROTECTED] writes:
> Hello,
>
> at the moment I am doing quite a lot of regression, especially
> logistic regression, on 2 or more records with 30 or more
> factors, using the "step" function to search for the model with the
> smallest AIC. This takes a lot of time on this 1.8 GHZ
Hello,
at the moment I am doing quite a lot of regression, especially
logistic regression, on 2 or more records with 30 or more
factors, using the "step" function to search for the model with the
smallest AIC. This takes a lot of time on this 1.8 GHZ Pentium
box. Memory does not seem t
