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

From: Rubén Roa <rroa_at_azti.es>
Date: Fri, 18 Jun 2010 08:34:55 +0200


	De: Chris Gast [mailto:cmgast_at_gmail.com] 
	Enviado el: jueves, 17 de junio de 2010 22:32
	Para: Rubén Roa
	CC: r-help_at_r-project.org; users_at_admb-project.org
	Asunto: Re: [ADMB Users] an alternative to R for nonlinear stat models
	
	
	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

If the problem originates in estimates being close to zero, a simple transformation (log or exp) might help. However, what I would really like to know is the performance of different methods of the optim function. I guess the reltol parameter and other control parameters would act different depending on the method, whether Nelder-Mead, CG, etc. I am currently experimenting with CG and although it is slow for my model, it has always produced hessian matrices that were invertible and with all diagonals positive after inversion (unlike Nelder-Mead, the default).

Rubén


Dr. Rubén Roa-Ureta
AZTI - Tecnalia / Marine Research Unit
Txatxarramendi Ugartea z/g
48395 Sukarrieta (Bizkaia)
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