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Have you looked into -xtmepoisson-?
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Keith Dear
(work)
Gesendet: Mittwoch, 24. Juni 2009 08:01
An: [email protected]
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
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F: 02 6125 0740
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