Despite nlmrt "solving" the OP's problem, I think Gabor's suggestion likely gives a more sensible approach to the underlying modelling problem.
It is, of course, sometimes important to fit a particular model, in which case nls2 and nlmrt are set up to grind away. And hopefully the follow-up to nlmrt I'm working on will have enough capability in getting analytic derivatives to work for a wider class of models. Note that functional approaches in nlmrt and minpack.lm allow users to provide derivatives. Too many users think numerical approximations are a panacea, but my experience is that most problems benefit from very accurate derivatives, of which analytic expressions are generally the best. JN On 16-10-09 09:24 PM, Gabor Grothendieck wrote: > If you are not tied to that model the SSasymp() model in R could be > considered and is easy to fit: > > # to plot points in order > o <- order(cl$Area) > cl.o <- cl[o, ] > > fm <- nls(Retention ~ SSasymp(Area, Asym, R0, lrc), cl.o) > summary(fm) > > plot(Retention ~ Area, cl.o) > lines(fitted(fm) ~ Area, cl.o, col = "red") > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
