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Re: st: xtmelogit slow, not converging


From   "JVerkuilen (Gmail)" <jvverkuilen@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: xtmelogit slow, not converging
Date   Wed, 19 Sep 2012 00:01:57 -0400

On Tue, Sep 18, 2012 at 7:58 PM, Valeria Fajardo
<valeriabutler@gmail.com> wrote:
>

<snip>

> Besides reducing the size of my sample, how can I make this run more
> smoothly? How much time should this type of routine be expected to
> take?

That sample size is quite large and you have a complicated model. It
could be quite a while. Try fitting it with fixed effects only first
to get rational starting values. Then add a random intercept and see
how long that takes. I'd put difficult on, too. With a relatively
complex GLMM you almost always have to go through a few simpler models
to get a feel for things and generate good starting values before
fitting the full model.

If you can't get that to work, you might want to sample from your
dataset to get the number to a smaller value, but I've run 100,000 on
a not all that impressive five year old MacBook Pro and Stata did the
job quite well in a not-crazy time period.

Sampling down to 10% of the size of your dataset to do specification
search isn't a bad idea anyway.



>I'm finding it so cumbersome I'm considering the switch to SPSS.

99.44% guarantee that will be worse and more cumbersome. Their GLMM
program is dicey in terms of what control it gives the user and very
slow. If you want to switch programs, try R and use lme4 or SAS with
GLIMMIX which will allow you to use the much faster but less accurate
PQL estimation, or get your hands on HLM, which has a higher order
Laplace approximation that is very fast. I really doubt you'll beat
Stata for estimation speed in the vast majority of cases, though.
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