Re: st: AW: gllamm (poisson) execution time

[Jeph Herrin <junk@spandrel.net>]

If you have a single random effect, you may find -xtpoisson-
is even faster than -xtmepoisson-.

hth,
Jeph


Keith Dear (home) wrote:
> Ummm ... no (well, NOW I have).
> Except on the uni supercomputer, we only have Stata9, hence ignorance. 
> Time to upgrade!
> Many thanks Martin.
> Keith
>
> ps
> http://www.stata.com/help.cgi?xtmepoisson
> http://stata.com/stata10/mixedmodels.html
>
>
>
> Martin Weiss wrote:
> > <>
> > Have you looked into -xtmepoisson-?
> >
> >
> >
> >
> > HTH
> > Martin
> >
> > -----Ursprüngliche Nachricht-----
> > Von: owner-statalist@hsphsun2.harvard.edu
> > [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Keith Dear
> > (work)
> > Gesendet: Mittwoch, 24. Juni 2009 08:01
> > An: statalist@hsphsun2.harvard.edu
> > Cc: Ainslie Butler
> > Betreff: st: gllamm (poisson) execution time
> >
> > We are trying to model daily mortality by poisson regression, over 17 
> > years, by postcode, with postcode as a single random intercept term.
> > In Stata10/MP4 on a linux cluster our models each take 7 or 8 hours to 
> > fit, which is too long to be feasible for exploratory analyses.
> >
> > The full dataset has >14 million rows of data: a row for every day for 
> > 1991-2007 for every postcode in Australia (~2200 postcodes), but to 
> > get things moving we are starting with smaller geographical regions of 
> > only 100 or 200 postcodes. Thus N=17*365*(100 or 200), about a half or 
> > one million. Also we are starting with failrly simple models, p=17 
> > fixed-effect parameters just for trend and annual cycles. The models 
> > converge ok, eventually, in only a few iterations and with typical 
> > condition number about 2.
> >
> > I found this in the list archives (from Sophia Rabe-Hesketh in 2003):
> > ==> biggest gain is to reduce M, followed by n, p and N
> > Here we have M=1, n=5 (down from the default of 8), p=17, but N=6E5 or 
> > more. There does not seem to be much prospect of reducing any of 
> > those, indeed we will need to substantially increase p (for more 
> > interesting models) and N (to cover all of Australia at once).
> >
> > Is there hope? Are there alternatives to gllamm for this? Or are we 
> > overlooking something basic here?
> > Keith
> >
> >   
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