[R] nonlinear regression with M estimation

[Prof Brian Ripley]

I don't think there is one.  One problem is that both nls and robust
procedures need a starting point and so you would need a good non-linear
resistant method to start.  (For certain Huber-type linear regressions you
can show there is a unique solution and so any starting point will do.  
But that is rather unusual.)

The nearest equivalent I can think of is package nlrq, which also needs 
suitable starting values.  Once you have those, you could just call optim 
to minimize the log-likelihood under the Huber long-tailed model.

On Mon, 5 Jul 2004, Ruei-Che  Liu wrote:

>    Could any one tells me if R or S has the capacity to fit nonlinear 
> regression with Huber's M estimation? Any suggestion is appreciated. I was
> aware of 'rlm' in MASS library for robust linear regression and 'nls' for 
> nonlinear least squares regression, but did not seem to be able to find 
> robust non-linear regression function.

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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