# [R] Nonlinear statistical modeling -- a comparison of R and AD Model Builder

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## [R] Nonlinear statistical modeling -- a comparison of R and AD Model Builder

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## Re: [R] Nonlinear statistical modeling -- a comparison of R and AD Model Builder

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## Re: [R] Nonlinear statistical modeling -- a comparison of R and AD Model Builder

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## Re: [R] Nonlinear statistical modeling -- a comparison of R and AD Model Builder

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## Re: [R] Nonlinear statistical modeling -- a comparison of R and AD Model Builder

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## Re: [R] Nonlinear statistical modeling -- a comparison of R and AD Model Builder

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## Re: [R] Nonlinear statistical modeling -- a comparison of R and AD Model Builder

 In reply to this post by Spencer Graves Spencer, I tried the mixed effects approach you suggest using the random effects module of AD Model Builder: (http://www.otter-rsch.ca/admbre/admbre.html). What are 94 unbounded parameters in Schnute et al (1998), now become realizations of a Gaussian random variable, with the corresponding standard deviation being estimated as a parameter. The approach works, but the computation time is increased substantially. This is however  understandable as the computational problem is a very different one. The likelihood function now involves an integral in dimension 94, which I believe cannot be broken into a product of lower dimensional integrals as is usual for clustered data (the reason being the recursive nature of the population dynamics). hans _______________________________ Spencer Graves wrote: >     Have you considered nonlinear mixed effects models for the types >of problems considered in the comparison paper you cite?  Those >"benchmark trials" consider "T years of data ... for A age classes and >the total number of parameters is m = T+A+5".  Without knowing more >about the problem, I suspect that the T year parameters and the A age >class parameters might be better modeled as random effects.  If this >were done, the optimization problem would then involve 7 parameters, the >5 fixed-effect parameters suggested by the computation of "m" plus two >variance parameters, one for the random "year" effects and another for >the random "age class" effect.  This would replace the problem of >maximizing, e.g., a likelihood over T+A+5 parameters with one of >maximizing a marginal likelihood over 2+5 parameters after integrating >out the T and A random effects. > >     These integrations may not be easy, and I might stick with the >fixed-effects solution if I couldn't get answers in the available time >using a model I thought would be theoretically more appropriate.  Also, >I might use the fixed-effects solution to get starting values for an >attempt to maximize a more appropriate marginal likelihood.  For the >latter, I might first try 'nlmle'.  If that failed, I might explore >Markov Chain Monte Carlo (MCMC).  I have not done MCMC myself, but the >"MCMCpack" R package looks like it might make it feasible for the types >of problems considered in this comparison.  The CRAN summary of that >package led me to an Adobe Acrobat version of a PPT slide presentation >that seemed to consider just this type of problem (e.g., > http://mcmcpack.wustl.edu/files/MartinQuinnMCMCpackslides.pdf). > >     Have you considered that? >     Hope this helps. >     Spencer Graves         [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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