[R] Constrained non linear regression using ML
rvaradhan at jhmi.edu
Tue Mar 16 21:06:33 CET 2010
I have an algorithm that can perform nonlinear optimization, with
linear/nonlinear, equality and/or inequality constraints. From your
description, it seems like this algorithm would work for your problem.
Contact me if you are interested and I will send you the code.
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Corrado
Sent: Tuesday, March 16, 2010 2:59 PM
To: r-help at r-project.org
Subject: [R] Constrained non linear regression using ML
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
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.
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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