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


From   jverkuilen <jverkuilen@gc.cuny.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: AW: gllamm (poisson) execution time
Date   Thu, 25 Jun 2009 10:27:46 -0400

Given your model (random intercept) you could use -xtpoisson-, which was in Stata 9 I believe. 

-----Original Message-----
From: "Keith Dear (home)" <keith.dear@anu.edu.au>
To: statalist@hsphsun2.harvard.edu
Cc: "Ainslie Butler" <ainslie.butler@anu.edu.au>
Sent: 6/24/2009 8:26 AM
Subject: Re: st: AW: gllamm (poisson) execution time

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
>
>   

-- 
Dr Keith Dear
Senior Fellow
National Centre for Epidemiology and Population Health
ANU College of Medicine, Biology and Environment
Building 62, cnr Mills and Eggleston Roads
Australian National University
Canberra ACT 0200 Australia 

T: 02 6125 4865
F: 02 6125 0740
M: 0424 450 396
W: nceph.anu.edu.au/Staff_Students/staff_pages/dear.php

CRICOS provider #00120C
http://canberragliding.org/

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