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, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
