[ADMB Users] an alternative to R for nonlinear stat models

[Chris Gast]

As an additional note, the parscale argument can be useful to improve
stability in convergence results in optim().

Chris



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


On Thu, Jun 17, 2010 at 1:32 PM, Chris Gast <cmgast at gmail.com> wrote:

> I spoke with my colleague who did most of the testing, and he has informed
> me that much of the hessian sensitivity actually came from a separate
> program (based on Numerical Recipes in C++ code) that did not use optim(),
> after having stopped using optim() due to speed issues.
>
> In my experience with optim, the reltol argument has improved important in
> this regard.  Very small changes in the parameter estimates at the converged
> solution (influenced by reltol) can lead to different standard error
> estimates by inverting the hessian, especially for parameter estimates close
> to zero (as vulnerability coefficients can be in many models with such a
> feature).  It is a limitation of the finite difference method for computing
> the hessian based on optimal parameter estimates.
>
>
>
> Chris
>
>
>
> -----------------------------
> Chris Gast
> cmgast at gmail.com
>
>
>
> On Wed, Jun 16, 2010 at 11:05 PM, Rubén Roa <rroa at azti.es> wrote:
>
>> ________________________________
>>
>> De: users-bounces at admb-project.org [mailto:users-bounces at admb-project.org]
>> En nombre de Chris Gast
>> Enviado el: miércoles, 16 de junio de 2010 21:11
>> Para: Arni Magnusson
>> CC: r-help at r-project.org; users at admb-project.org
>> Asunto: Re: [ADMB Users] an alternative to R for nonlinear stat models
>>
>> Hi Arni (and others),
>>  My dissertation work involves use (and extension) of models of the same
>> ilk (sometimes exactly the same) as those described by Nancy Gove and John
>> Skalski in their 2002 article.  I began with R, and moved to my own
>> home-brewed C/C++ programs for the sake of of speed when fitting models and
>> real and simulated data.  In addition, we found that the estimated standard
>> errors (based on the inverse hessian output from optim()) were very
>> sensitive to tolerance criteria--often changing orders of magnitude.
>>
>>
>> Hi,
>> Regarding the last bit, optim() has several methods (Nelder-Mead,
>> simulated annealing, conjugate gradient, etc). It is interesting to me which
>> method produced what result with the standard errors from the inverse
>> Hessian. Can you briefly ellaborate?
>> Thanks
>> Rubén
>>
>>
>> ____________________________________________________________________________________
>>
>> Dr. Rubén Roa-Ureta
>> AZTI - Tecnalia / Marine Research Unit
>> Txatxarramendi Ugartea z/g
>> 48395 Sukarrieta (Bizkaia)
>> SPAIN
>>
>>
>
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