[ADMB Users] Restricting magnitude of random effects estimates, achieving convergence of RE models

Tue Aug 3 16:37:00 PDT 2010

Thanks Mark.  That's interesting that it solved the problem you were
having--I would definitely call that unexpected behavior. I believe,
however, that I am in this situation already--the only non-objective
function calculations I have are an abundance reconstruction based on
estimated parameters, but I put this in the report section in the interest
of speed and efficiency, so it would only run once (right?).

Chris

-----------------------------
Chris Gast
cmgast at gmail.com

On Tue, Aug 3, 2010 at 2:48 PM, Mark Maunder <mmaunder at iattc.org> wrote:

>  Chris,
>
>
>
> Not sure if this helps, but I was having the same problem with a model I
> was running for impact analysis. I had two models running simultaneously one
> with the covariates estimated and one with them fixed at zero to determine
> the impact of the covariates. Since the second model shared the parameters
> of the first model but did not fit to any data, it should not influence the
> results. I got the nan, so turned off the second model and it worked. Then
> shifted the second model to only get called from the report section and it
> still worked.
>
>
>
> So you might want to place any calculations that are not required to
> calculate the objective function in function called from the report section
> (and/or called only if in the sd_phase()).
>
>
>
> Hope this helps,
>
>
>
> Mark
>
>
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>
>
> Mark Maunder
>
> Head of the Stock Assessment Program
>
> Inter-American  Tropical Tuna Commission
> 8604 La Jolla Shores Drive
> La Jolla, CA, 92037-1508, USA
>
> Tel: (858) 546-7027
> Fax: (858) 546-7133
> mmaunder at iattc.org
>
> http://www.fisheriesstockassessment.com/TikiWiki/tiki-index.php?page=Mark+Maunder
>
>
>
> Visit the AD Model Builder project at
>  http://admb-project.org/
>
>
>
> See the following website for information on fisheries stock assessment
>
> http://www.fisheriesstockassessment.com/
>
>
>
> *From:* users-bounces at admb-project.org [mailto:
> users-bounces at admb-project.org] *On Behalf Of *Chris Gast
> *Sent:* Tuesday, August 03, 2010 2:28 PM
> *To:* users at admb-project.org
> *Subject:* [ADMB Users] Restricting magnitude of random effects
> estimates,achieving convergence of RE models
>
>
>
> Hello again,
>
>
>
> I'm simulating age-at-harvest data (and accompanying effort data) and
> trying to fit a series of 12 models, the most complex of which contains 3
> random effects vectors (all normally-distributed).  I'm varying the
> dimensionality of the problem, but my current scenario involves random
> effects vectors of dimension ~25.  There are also approximately 15 to 40
> fixed parameters (6 of which are means and standard deviations corresponding
> to the random effects vectors).
>
>
>
> A frequent problem I've encountered is that during estimation, ADMB often
> elevates the magnitude of random effects estimates such that the objective
> function value enters NaN territory, from which it cannot recover.  I've
> tried using random_effects_bounded_vectors, but this frequently leads to
> optimization failure ("hessian does not appear to be positive definite"),
> regardless of the magnitude of the limits I impose.  I've concocted a
> penalty function that helps alleviate this problem (most of the time): Prior
> to multiplying the log-likelihood by -1, I subtract 10 times the sum of
> squared random effects estimates.  In code, this looks like:
>
>
>
> ....previous log-likelihood computations....
>
>
>
> sumt=0;
>
> for(i=0;i<nyears;i++){
>
> sumt=sumt+t[i]*t[i];
>
> }
>
> totL -= sumt*10;
>
>
>
> totL *= -1;
>
>
>
> where t is defined as a random_effects_vector, sumt is a dvariable, and
> totL is the objective function value.  Sometimes a value of 10 works, and
> sometimes an unreasonable (but equally arbitrary) value of 100,000 is
> necessary to obtain convergence.
>
>
>
> Prior to this code, I use the usual
>
>
>
> totL += -(nyears)*log(csigma)-.5*norm2(t/csigma);
>
>
>
> or alternatively
>
>
>
> totL  += -.5*norm2(t);
>
> tt = csigma*t;
>
>
>
> with appropriate definitions for the variance parameter csigma, and t and
> tt.  I'll also note that each of the random effects occurs within either an
> exponential or logistic transformation of some demographic process.
>
>
>
> Of course, the higher the arbitrary scale factor (10 - 100,000), the
> greater restriction I am placing on the variance parameter, csigma.  This is
> a parameter of some interest for me, and I don't want to limit its range.
>
>
>
> I'm willing to accept that some models will fail to fit, particularly
> because many models are simplifications of the true simulation model.  The
> problem is that to obtain a reasonable number of "successful" simulations, I
> need to limit the failure rate of such models.
>
>
>
> Does anyone have some experience with such a problem that they'd be willing
> to share? How have others dealt with problems of this nature?  Is there some
> customary penalty function of which I'm unaware?
>
>
>
>
>
>
>
> Thanks very much,
>
>
>
> Chris Gast
>
> University of Washington
>
> Quantitative Ecology and Resource Management
>
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>
> -----------------------------
> Chris Gast
> cmgast at gmail.com
>
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