Date: Thu, 12 Apr 2007 13:22:14 -0700
Reply-To: Dale McLerran <stringplayer_2@YAHOO.COM>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Dale McLerran <stringplayer_2@YAHOO.COM>
Subject: Re: PAPER ON ZERO-INFLATED NEGATIVE bINOM
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--- 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?
Fred Hutchinson Cancer Research Center
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