Dear R users,
I have to fit the non linear regression:
y~1-exp(-(k0+k1*p1+k2*p2+ .... +kn*pn))
where ki>=0 for each i in [1 .... n] and pi are on R+.
I am using, at the moment, nls, but I would rather use a Maximum
Likelhood based algorithm. The error is not necessarily normally
distributed.
y is approximately beta distributed, and the volume of data is medium to
large (the y,pi may have ~ 40,000 elements).
I have studied the packages in the task views Optimisation and Robust
Statistical Methods, but I did look like what I was looking for was
there. Maybe I am wrong.
The nearest thing was nlrob, but even that does not allow for
constraints, as far as I can understand.
Any suggestion?
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
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