Re: [R] nonlinear least squares fitting Trust-Region"

From: Spencer Graves <spencer.graves_at_pdf.com>
Date: Sat 02 Sep 2006 - 18:04:53 GMT

      May I also suggest Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley). This book carefully explains the difference between "parameter effects" and "intrinsic" curvature in non-linear fitting. I don't know if this idea was original with Bates or Watts, but I believe that Bates' PhD dissertation made important, original contributions to our understanding of it -- and it helped get him the faculty position in Statistics at the University of Wisconsin, where he still is. Bates is also a leading contributor to R.

      hope this helps. 
      spencer graves

RAVI VARADHAN wrote:
> As suggested by Prof. Ripley, you should read a good book in the optimization area. One that I would highly recommend is the book by Dennis and Schnabel (1983) - Numerical methods for unconstrained optimization, which does a great job of explaining both "line-search" and "trust-region" approaches for achieving globally-convergent versions of a fast numerical scheme such as Gauss-Newton.
>
> Best,
> Ravi.
>
> ----- Original Message -----
> From: Prof Brian Ripley <ripley@stats.ox.ac.uk>
> Date: Saturday, September 2, 2006 5:51 am
> Subject: Re: [R] nonlinear least squares fitting Trust-Region"
> To: Martin Ivanov <tramni@abv.bg>
> Cc: r-help@stat.math.ethz.ch
>
>
>> I believe people (including me) did not reply because you appeared
>> not to
>> have done your homework. The help page for ?nls _does_ have a
>> reference
>> to the 'port' documentation, and RSiteSearch("trust region") is
>> informative and leads to an R package that does trust-region
>> optimization.
>> (So would looking in the R FAQ.)
>>
>> You say:
>>
>>
>>> Since I am not an expert in the field of optimization, I am just
>>> conforming to what matlab documentation
>>>
>> Please note that some of the R developers are really expert in
>> that area,
>> and their advice (in the R documentation) should be taken as
>> seriously as
>> that in some commercial package that is merely commenting about
>> the very
>> sparse choice it offers. Or if R is not in your personal trust
>> region,
>> just use 'matlab'.
>>
>> Please
>>
>> 1) do not shout at your helpers: using all caps is regarded as
>> shouting.
>> 2) study and follow the posting guide. People are much more
>> likely to
>> help you if you demonstrate you have made efforts to help yourself.
>>
>> 3) read the literature. The R FAQ leads to books that cover
>> fitting
>> non-linear models in S/R in considerable detail.
>>
>>
>> On Sat, 2 Sep 2006, Martin Ivanov wrote:
>>
>>
>>> Dear Mr Graves,
>>>
>>> Thank you very much for your response. Nobody else from this
>>>
>> mailing
>>
>>> list ventured to reply to me for the two weeks since I posted my
>>> question. "nlminb" and "optim" are just optimization procedures.
>>>
>> What I
>>
>>> need is not just optimization, but a nonlinear CURVE FITTING
>>>
>> procedure.
>> Which is just optimization: usually by least squares (although you
>> have
>> not actually specified that and there are better modern
>> statistical
>> ideas).
>>
>>
>>> If there is some way to perform nonlinear curve fitting with the
>>> "Trust-Region" algorithm using any of these functions, I would
>>>
>> me much
>>
>>> obliged to you if you suggest to me how to achieve that. You
>>>
>> asked me
>>
>>> why I do not want Gauss-Newton. Since I am not an expert in the
>>>
>> field of
>>
>>> optimization, I am just conforming to what matlab documentation
>>> suggests, namely: "Algorithm used for the fitting procedure:
>>> Trust-Region -- This is the default algorithm and must be used
>>>
>> if you
>>
>>> specify coefficient constraints. Levenberg-Marquardt -- If the
>>> trust-region algorithm does not produce a reasonable fit, and
>>>
>> you do not
>>
>>> have coefficient constraints, you should try the Levenberg-
>>>
>> Marquardt
>>
>>> algorithm. Gauss-Newton --THIS ALGORITHM IS POTENTIALLY FASTER
>>>
>> THAN THE
>>
>>> OTHER ALGORITHMS, BUT IT ASSUMES THAT THE RESIDUALS ARE CLOSE TO
>>>
>> ZERO.
>>
>>> IT IS INCLUDED FOR PEDAGOGICAL REASONS AND SHOULD BE THE LAST
>>>
>> CHOICE FOR
>>
>>> MOST MODELS AND DATA SETS. I browsed some literature about the
>>>
>> garchfit
>>
>>> function, but I did not see the "Trust-Region" algorithm there
>>>
>> either:
>>
>>> algorithm = c("sqp", "nlminb", "lbfgsb", "nlminb+nm",
>>>
>> "lbfgsb+nm"),
>>
>>> control = list(), title = NULL, description = NULL, ...)
>>>
>>> Thank you for your attention. I am looking forward to your reply.
>>> Regards,
>>> Martin
>>>
>>> -----------------------------------------------------------------
>>> vbox7.com - ??????? ????? ???????!
>>>
>>> ______________________________________________
>>> R-help@stat.math.ethz.ch mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide http://www.R-
>>>
>> project.org/posting-guide.html
>>
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>>
>> --
>> Brian D. Ripley, ripley@stats.ox.ac.uk
>> Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
>> University of Oxford, Tel: +44 1865 272861 (self)
>> 1 South Parks Road, +44 1865 272866 (PA)
>> Oxford OX1 3TG, UK Fax: +44 1865 272595
>>
>> ______________________________________________
>> R-help@stat.math.ethz.ch mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-
>> guide.htmland provide commented, minimal, self-contained,
>> reproducible code.
>>
>>
>
> ______________________________________________
> R-help@stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>



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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Sun Sep 03 04:12:48 2006

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