________________________________
De: Chris Gast [mailto:cmg...@gmail.com]
Enviado el: jueves, 17 de junio de 2010 22:32
Para: Rubén Roa
CC: r-help@r-project.org; us...@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)
SPAIN
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