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

From: Rubén Roa <rroa_at_azti.es>
Date: Thu, 17 Jun 2010 08:05:06 +0200


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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