You could try method="brute-force" in the nls2 package to find starting values.
On Tue, Mar 30, 2010 at 7:03 AM, Corrado <ct...@york.ac.uk> wrote: > I am using nls to fit a non linear function to some data. > > The non linear function is: > > y= 1- exp(-(k0+k1*p1+ .... + kn*pn)) > > I have chosen algorithm "port", with lower boundary is 0 for all of the ki > parameters, and I have tried many start values for the parameters ki > (including generating them at random). > > If I fit the non linear function to the same data using an external > algorithm, it fits perfectly and finds the parameters. > > As soon as I come to my R installation (2.10.1 on Kubuntu Linux 910 64 bit), > I keep getting the error: > > Error in nlsModel(formula, mf, start, wts, upper) : singular gradient > matrix at initial parameter estimates > > I have read all the previous postings and the documentation, but to no > avail: the error is there to stay. I am sure the problem is with nls, > because the external fitting algorithm perfectly fits it in less than a > second. Also, if my n is 4, then the nls works perfectly (but that excludes > all the k5 .... kn). > > Can anyone help me with suggestions? Thanks in advance. > > Alternatively, what do you suggest I should do? Shall I abandon nls in > favour of optim? > > Regards > > -- > Corrado Topi > PhD Researcher > Global Climate Change and Biodiversity > Area 18,Department of Biology > University of York, York, YO10 5YW, UK > Phone: + 44 (0) 1904 328645, E-mail: ct...@york.ac.uk > > ______________________________________________ > R-help@r-project.org 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@r-project.org 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.
