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.
