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From |
Michael Ingre <Michael.Ingre@ipm.ki.se> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: question on GLLAMM |

Date |
Thu, 19 Aug 2004 21:08:15 +0200 |

On 2004-08-19, at 16.53, Stanislav Kolenikov wrote:

There are however techniques to speed up -gllamm- that can be extremely efficient with large datsets, if the number of response patterns are limited. Lets say you have 10.000 observations but only 5*3*10 possible response patterns; if you use -contract , freq(_freq)- you end up with only 150 observations with varying frequency weights. This dataset should be estimated with -gllamm , weight(_freq)- in about the same amount of time as a dataset with only 150 individuals.If you have a dataset of say 10000 individuals, and you also want to take some sample design clustering into account... you are doomed to wait for a few weeks for the model to converge.

That is so true. It just happened that two days ago I got a paper accepted for publication that summarise 323 hours (!) of computing time (Mac G4 800Mhz). A total of 10 models of varying complexity and dependent variables were estimated, 3 binary (logit) and 7 ordinal (ologit). The data was longitudinal with 60 repeated measures of 17 subjects - 1020 obs. With only two random effects I expect the models to converge in about 1/10 of the time. With 4 random effects I expect one model to converge in 200-300 hours and with 5+ random effects I probably need a Cray (or maybe a G5).unless you have the latest Cray at your disposal, the model should be kept to a moderate size. [ .... ] (It took a few days with -oprobit- link and the panel structure with just one random effect on my computer.)

Michael

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**Follow-Ups**:**Re: st: question on GLLAMM***From:*Michael Ingre <Michael.Ingre@ipm.ki.se>

**References**:**Re: st: question on GLLAMM***From:*"Stanislav Kolenikov" <skolenik@yahoo.com>

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