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Re: st: -gllamm- vs -meglm-

From   Stas Kolenikov <>
To   "" <>
Subject   Re: st: -gllamm- vs -meglm-
Date   Wed, 3 Jul 2013 14:28:19 -0500

Just from your description, NRRIDGE seems to be equivalent to Stata's
-difficult- maximization option, see -help maximize-. I don't think
there's anything directly resembling the RSPL method (which, I think,
is only good to produce the starting values for a more rigorous
method), but the (quick and dirty) Laplacian approximation
-intmethod(laplace)- is probably the closest you can get in terms of
simplifying the task to the extent possible.

-- Stas Kolenikov, PhD, PStat (SSC)
-- Senior Survey Statistician, Abt SRBI
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer

On Wed, Jul 3, 2013 at 2:03 PM, Daniel Waxman <> wrote:
> 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)."*
> ------------------------------------------------------------------
> 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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