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Re: st: NASUG'2003 Wishes and Grumbles

From   Joseph Coveney <[email protected]>
To   Statalist <[email protected]>
Subject   Re: st: NASUG'2003 Wishes and Grumbles
Date   Fri, 04 Apr 2003 23:05:16 +0900

Ian Dohoo wrote in response to my wishes and grumbles for enhanced capabilities for 
mixed models in Stata:


I am in complete agreement with the need for an equivalent 
to SAS Proc Mixed (for my work - currently the only serious 
limitation in Stata)

However, I believe that -gllamm- (written by Sophia Rabe-
Hesketh) is not just an alternative to NLMIXED, but is in fact 
more flexible (ie gllamm can handle n-level data while 
NLMIXED is limited to 2-level data).


Couple of things:

First, I feel that I was gratuitously harsh in my earlier posting.  When the comment was 
raised in the wishes and grumbles session, Bill Gould was in complete sympathy.  In 
addition, at the NASUG meeting, an undocumented capability in Stata 8 was disclosed 
that blazes the path for Stata for mixed models, for which at least some subroutines 
need to be in compiled code (C, Fortran), and not as interpreted ado-file code, in order 
to execute in a reasonable time and in reasonable precision.  (This is the tactic that S-
Plus uses in its lme and nlme.)  In addition, last December 23rd, Bobby Gutierrez (from 
StataCorp) wrote in response to a query, "Hierachical models are at a high priority for 
us at Stata Corp., and we will continue to work towards making them part of official 
Stata."  So we know that this project is well underway.  (I recall that there was a more 
recent posting from someone at StataCorp to the effect that they were targeting this 
spring for a milestone on the project, but I can't locate the posting in the archives now.)

Second, I agree with Ian about -gllamm-'s superiority over PROC NLMIXED as to 
flexibility in the number of levels.  -gllamm- is a gem of a command.  But one feature 
that -gllamm- lacks (at least to those like me who don't know how to hack its code to get 
it to do it) is the ability to fit arbitrary nonlinear functions so long as you can specify the 
function � la -nl-.  With PROC NLMIXED, you specify the nonlinear function and its 
fixed and (normally distributed) random effects, and it will try to fit the model.  This 
enables, for example, multi-exponential pharmacokinetic models to be fit.  In my 
posting, I was going to mention something to the effect that, if -gllamm- could be 
opened up so that users could specify an arbitrary nonlinear model analogously to 
PROC MIXED, then it would be leagues ahead of what SAS or S-Plus has to offer.

Joseph Coveney

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