Date: Thu, 12 Apr 2007 19:04:35 -0400
Reply-To: Dave Fournier <otter@OTTER-RSCH.COM>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Dave Fournier <otter@OTTER-RSCH.COM>
Subject: Re: PAPER ON ZERO-INFLATED NEGATIVE bINOM
On Thu, 12 Apr 2007 13:22:14 -0700, Dale McLerran <stringplayer_2@YAHOO.COM>
>--- Dave Fournier <otter@OTTER-RSCH.COM> wrote:
>> On Wed, 11 Apr 2007 17:26:20 -0700, Dale McLerran
>> >What is the interpretation of these parameters? Since they are
>> >all negative, I know that none can be random effect variance
>> >estimates. I suspect that the last two are log(V(int)) and
>> >log(V(slope)), but I really can't tell. That would leave the
>> >first parameter in tmpL to be some function of the covariance,
>> >but what function?
>> >Dale McLerran
>> >--- Dave Fournier <otter@OTTER-RSCH.COM> wrote:
>> The code on our web site is more general negative binomial model
>> code we had been developing for our R model. It was not optimized
>> for the epilepsy data, and does not exploit the simple structure of
>> that problem. That is why it took so long (45 seconds you say) to
>> Also it is calculating just the Laplace approximation not
>> adaptive Gauss-Hermite integration which may explain the difference
>> in our
>> log-likelihood values. I have put the code
>> which we used for our paper which does exploit the simple structre
>> for this example up on my web site at
>> You can run a say 50 point adaptive gauss-Hermite integration with
>> epil -ainp epil.par -gh 50
>> On my computer this take under 10 seconds so that it appears that
>> AD Model builder is about 50 times faster than SAS NLMIXED for this
>> and has a log-like value of
>> I get the same value for 100 points as well. How many points did you
>I didn't specify the number of quadrature points. I just used the
>NLMIXED default behavior which allows NLMIXED to adaptively
>select the number of quadrature points.
>> I look forward to hearing aobut the performance of your optimzed SAS
>> code on
>> this problem.
>To be honest, I really am not interested in spending time to
>optimize the NLMIXED. I have too many other things to do right
>now. I am willing to cede that ADMB is faster than NLMIXED for
>the given problem (and many other problems that you have presented
>in the past in this forum).
>> Without the -gh 50 i.e. just Laplace approximation it takes under 5
>> and produces the estimate -624.551 as before.
>> To understand the results look in the epil.std file where the
>> with their estimated standard deviations are reported.
>I think you mean the nbmm.std file. There is no epil.std file
>produced. I had already looked at that file and the tmpL vector
>is the same there as in the file nbmm.par. But neither informs
>me how to interpret this vector. Since all of the values are
>negative, this is not a covariance matrix estimate. However, I
>think that it is a reparameterization of the covariance matrix.
>I would note, too, that the file nbmm.std includes a parameter
>sigma. What is this sigma? How does it enter the model?
I'm sorry, I did not mean to irritate you. I did not bring up the
matter of comparative timings -- you did, and implied that maybe
the ADMB code was faster because it was optimized for the problem.
I simply responded to that with the observation that the nbmm.exe
on the web site was not the optimized code we used in our
comparisons. Also you referred to the difference in log-likelihood values
which necessitated my bringing up the the matter of the optimized
model as only it could
do the Gauss-Hermite integration so that a comparison with SAS could be
done. Finally you first mentioned that you could do better with
optimzied SAS code for the model, not I, but I'm sure you have better things
to do with your time.
The file epil.std is in the zip file on my web site which I referred to in
my last post. I'll send it to you off list as I assume that everone else has
better things to do as well.
But ... you would think someone would be interested if ADMB is really 50
times faster than SAS for this problem (not saying it is yet of course) or
maybe people have nothing better to do than to watch their SAS programs running.