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Constrained non linear regression using ML

Corrado Topi 
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

Thread (11 messages)

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