[R] Error "singular gradient matrix at initial parameter estimates" in nls

[Gabor Grothendieck]

You could try method="brute-force" in the nls2 package to find starting values.

On Tue, Mar 30, 2010 at 7:03 AM, Corrado <ct529 at 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: ct529 at york.ac.uk
>
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