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

[Corrado]

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