[ADMB Users] an alternative to R for nonlinear stat models
rroa at azti.es
Wed Jun 16 23:05:06 PDT 2010
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.
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?
Dr. Rubén Roa-Ureta
AZTI - Tecnalia / Marine Research Unit
Txatxarramendi Ugartea z/g
48395 Sukarrieta (Bizkaia)
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