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From |
Jeph Herrin <junk@spandrel.net> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Thu, 25 Jun 2009 10:43:16 -0400 |

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 timeWe are trying to model daily mortality by poisson regression, over 17years, 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 tofit, which is too long to be feasible for exploratory analyses.The full dataset has >14 million rows of data: a row for every day for1991-2007 for every postcode in Australia (~2200 postcodes), but toget things moving we are starting with smaller geographical regions ofonly 100 or 200 postcodes. Thus N=17*365*(100 or 200), about a half orone million. Also we are starting with failrly simple models, p=17fixed-effect parameters just for trend and annual cycles. The modelsconverge ok, eventually, in only a few iterations and with typicalcondition 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 NHere we have M=1, n=5 (down from the default of 8), p=17, but N=6E5 ormore. There does not seem to be much prospect of reducing any ofthose, indeed we will need to substantially increase p (for moreinteresting models) and N (to cover all of Australia at once).Is there hope? Are there alternatives to gllamm for this? Or are weoverlooking something basic here?Keith

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**Follow-Ups**:**Re: st: AW: gllamm (poisson) execution time***From:*"Keith Dear (home)" <keith.dear@anu.edu.au>

**References**:**st: Interpreting Poisson output***From:*"Data Analytics Corp." <dataanalytics@earthlink.net>

**st: RE: Interpreting Poisson output***From:*"Kieran McCaul" <Kieran.McCaul@uwa.edu.au>

**st: gllamm (poisson) execution time***From:*"Keith Dear (work)" <keith.dear@anu.edu.au>

**Re: st: AW: gllamm (poisson) execution time***From:*"Keith Dear (home)" <keith.dear@anu.edu.au>

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