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

Wed Jun 16 12:18:48 PDT 2010

Arni and Chris,

Thanks for the information about Bugs.

Attached is a paper that describes the use of software programs in fisheries stock assessment. It agrees with much of what you have been discussing. It also talks about other software that is also used in combination with ADMB. I personally use ADMB for estimation, R for graphics, and Excel for simple calculations and model checking. 

Mark

Mark Maunder 

Head of the Stock Assessment Program

Inter-American  Tropical Tuna Commission
8604 La Jolla Shores Drive
La Jolla, CA, 92037-1508, USA

Tel: (858) 546-7027
Fax: (858) 546-7133
mmaunder at iattc.org
http://www.fisheriesstockassessment.com/TikiWiki/tiki-index.php?page=Mark+Maunder

Visit the AD Model Builder project at
 http://admb-project.org/ <http://admb-project.org/> 

See the following website for information on fisheries stock assessment

http://www.fisheriesstockassessment.com/

From: users-bounces at admb-project.org [mailto:users-bounces at admb-project.org] On Behalf Of Chris Gast
Sent: Wednesday, June 16, 2010 12:11 PM
To: Arni Magnusson
Cc: r-help at r-project.org; users at admb-project.org
Subject: 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.

I have recently made the move to ADMB to make use of the capabilities for mixed effect models.  One of my current tasks involves simulating data (in R), fitting models in ADMB via the shell() command in R, and importing .rep files into R for processing results of multiple simulations.  In this respect, I too describe my working environment as "R with ADMB," and understand the relative benefits of this setup.  For these specific types of models, I've found ADMB to be irreplaceable.  Similarly, I could not easily summarize simulation results without R; both are vital components to the work I'm doing.

With respect to your benchmarking suggestion:  I have many implementations of models for real and simulated data in both R and ADMB (past and current iterations of my work on the models described above), and may be able to complete some of this work alongside my own work, although I don't yet claim that either my R or ADMB implementations are completely optimized for speed.

Chris

-----------------------------
Chris Gast

University of Washington

Quantitative Ecology and Resource Management
cmgast at gmail.com

On Wed, Jun 16, 2010 at 11:39 AM, Arni Magnusson <arnima at hafro.is> wrote:

As far as I can tell, Gove et al. (2002) might be a good example for benchmarking the optimization performance of R vs. ADMB. It would be great if expert R users/developers could tweak Beni's model so that the performance comparison is valid.

The main purpose is not to see which is faster or more reliable, but to quantify how much performance is gained by moving from R to ADMB.

I have just typed and uploaded an excerpt from an old but thorough benchmark, where ADMB outperformed Gauss, Matlab, and S-Plus (http://admb-project.org/community/benchmarks/optimization). The benchmark was performed in 1997 and an update would be very valuable, as software and hardware have improved since then.

R is an interpreted language that can do just about anything. ADMB is a compiled language (thin layer on top of C++) that does optimization and nothing else. I don't think it is a realistic goal for R to match the optimizing performance of ADMB. I use R for most of my work, but crunch numbers with ADMB when computational speed and flexibility become an issue.

As an analogy, data frames in R are great, along with functions like aggregate(), apply(), merge(), and xtabs(). But when the tables are too large and too many, it's time to delegate the problem to a relational database.

In the case of model X, which is already implemented in R, it may take half an hour or half a month to convert it to ADMB, and the payoff depends on how often the model needs to be run. In the case of model Y, which is not yet implemented, some users may be quicker to implement it in R than in ADMB, so again it would be good to have an idea about the relative performance gain. Model Z may not run at all in R, due to its size and complexity.

Although I started this email with "R vs. ADMB", my daily working environment is better described as "R and ADMB". I'm a regular contributor to both R (4 packages and a couple of functions in the base packages) and ADMB (dev core team). Others have contributed R packages to interface with ADMB (http://admb-project.org/community/admb-meeting-march-29-31/InterfacingADMBwithR.pdf/at_download/file), but for many the interface is just reading and writing text files.

I fully appreciate the comfort and efficiency of the R working environment, and the benefits of performing most tasks inside the same environment. But the R community has no need to be on the defense against ADMB, any more than against relational databases. If you work with computationally intensive models, I encourage you to try out ADMB (admb-project.org, free software) and hopefully end up contributing ideas and/or code.

Best regards,

Arni

P.S. Phew. Future emails mentioning ADMB on r-help can be more brief and to the point, citing this message for details.

Windows ADMB-IDE installer
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