st: gllamm (poisson) execution time

["Keith Dear (work)" <keith.dear@anu.edu.au>]

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
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