On Tue, 16 Jan 2007, Turner, Heather wrote: > Hi Johann, > > The current version of gnm is unable to fit this type of model, though a > new version with more flexibility is soon to be released. > > In any case, you probably want to use nls or gnls, depending on the > assumptions that can be made about the model errors. For nls it is usual > to assume that the errors are normally distributed with mean zero and > constant variance, though the normal assumption is not strictly > necessary. If you have reason to think the errors are correlated and/or > have unequal variances, then gnls would be appropriate.
nls is able to handle unequal variances since 2.3.0: from the help weights: an optional numeric vector of (fixed) weights. When present, the objective function is weighted least squares. > > The examples on ?nls may be enough to get you started, > > Heather > > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Johann Hibschman > Sent: 16 January 2007 04:05 > To: Turner, Heather; r-help > Subject: [R] nonlinear regression: nls, gnls, gnm, other? > > Hi all, > > I'm trying to fit a nonlinear (logistic-like) regression, and I'd like > to get some recommendations for which package to use. > > The expression I want to fit is something like: > > y ~ A * exp(X * Beta1) / (1 + exp(-(x + X * Beta2 - xmid)/scal)) > > Basically, it's a logistic function, but I want to be able to modify > the saturation amplitude by a few parameters (Beta1) and shift the > inflection point around with a few other parameters (Beta2). I have a > ton of data, but I often have trouble getting the routine to fit. > (I've been using nlin in SAS, which seems sloppier in terms of > accepted convergence.) > > Now, from what I can tell, I can use nls, gnls, or gnm to fit > something like this, but I can't tell which would be better, or if > there's something else I should be trying. To do this right, though, > I have to do a lot more reading, but I'd like to know where to start. > > (I have more of a physics/computer background, so I immediately jump > to thinking of regression as minimizing some cost function across a > multidimensional space and then start mumbling about simulated > annealing or some such, but this isn't helping me much in interpreting > the available literature.) > > So, does anyone have any suggestions? I imagine I'm going to have to > pick up a book, but should it be Pinheiro & Bates on nlme, Bates & > Watts, the pdf manual to gnm, or what? > > Thanks for any suggestions, > > Johann > > ______________________________________________ > 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. > > ______________________________________________ > 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, [EMAIL PROTECTED] 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.html and provide commented, minimal, self-contained, reproducible code.