[R] nonlinear least squares trust region fitting ?

Spencer Graves spencer.graves at pdf.com
Sat Sep 2 10:12:34 CEST 2006

      1.  "Port" is NOT and R package but something more generally 
available.  I just got 189 hits from Google for "nl2sol Port package", 
some of which should answer your questions about that. 

      2.  Have you considered 'nlminb' and 'optim'?  There is also a 
"sequential quadratic programming" algorithm embedded in the code for 

      3.  If you'd like more help from this listserve, please post 
another question.  When you do so, please provide commented, minimal, 
self-contained, reproducible code, as suggested in the posting guide 
"www.R-project.org/posting-guide.html".  Please also help us understand 
why you don't want Gauss-Newton -- in prose as simple and clear as 
possible.  Doing so will increase your chances of a prompt reply that 
will likely be closer to what you want. 

      Hope this helps. 
      Spencer Graves

Martin Ivanov wrote:
> Hello!
> I am running R-2.3.1-i386-1 on Slackware Linux 10.2. I am a former matlab user, moving to R. In matlab, via the cftool, I performed nonlinear curve fitting using the method "nonlinear least squares" with the "Trust-Region" algorithm and not using robust fitting. Is it possible to perform the same analysis in R? I read quite a lot of R documentation, but I could not find an alternative solution. If there is such, please forgive my ignorance (I am a newbie in R) and tell me which function from which package is capable of performing the same analysis. If the same analysis is not possible to carry out in R, I would be grateful if you suggest to me some alternative procedure. I found that the "nls" function performs nonlinear least squares. The problem is that I do not want to implement the Gauss-Newton algorithm. In the worst case I would be contented with the "Levenberg-Marquardt" algorithm, if it is implemented in R. R nls's documentation mentions the "port" package and the ‘nl
>  2sol’ algorithm, but I could not find that package in the CRAN repository, so that I could read and judge whether that algorithm would be appropriate.
> Thank you very much in advance. I am looking forward to your answer.
> Regards,
> Martin
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