Re: st: -gllamm- vs -meglm-

[Daniel Waxman <dan@amplecat.com>]

Joseph Coveney wrote:

Just for clarification, is PROC GLIMMIX fast and light on gigabyte-sized
datasets even when it's using seven-abscissa adaptive Gauss-Hermite quadrature
as its estimation method?  According to its documentation, "The default
estimation technique in generalized linear mixed models is residual
pseudo-likelihood with a subject-specific expansion (METHOD=RSPL)."*

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Joseph and Tim, thanks for your replies.

I can't speak GLIMMIX's performance using that particular estimation
method; the method that I've been using is called "NRRIDGE"
(Newton-Raphson with Ridging).   To give an example, I just ran a
model  with 186 variables, a random intercept with 5,269 groups, and
270,684 observations (a 1% sample), using 1.3 seconds of CPU time!  So
far I haven't been able to get this to run at all in Stata, even using
the numerical integration options.  For me, it's all about the
destination, not the journey, meaning that I couldn't care less what
sort of estimation technique is used as long as the results are
correct.  If two methods produce correct results and one takes minutes
and the other takes hours or fails to converge at all, then I'll take
the first one.

Of course, the validity of the results might be the rub.  Does anybody
know of a good reason to be wary of the NRRIDGE algorithm?

I've been a long-time Stata fan; believe me, I'd love to never have to
use anything else.   But data seems to be getting bigger faster than
memory is getting cheaper, so the jury still seems to be out as to
whether that is going to be possible.

Dan
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