On Tue, 21 Feb 2006, Peter Dalgaard wrote: > "Liaw, Andy" <[EMAIL PROTECTED]> writes: > >> Your understanding isn't similar to mine. Mine says robust/resistant >> methods are for data with heavy tails, not heteroscedasticity. The common >> ways to approach heteroscedasticity are transformation and weighting. The >> first is easy and usually quite effective for dose-response data. The >> second is not much harder. Both can be done in R with nls(). > > And there is gnls() which allows direct modelling of the variance.
in package nlme, BTW. R-devel allows weights in nls, which makes it easier for those most familiar with that function. > > -p > >> Andy >> >> From: Quin Wills >>> >>> I am using "nls" to fit dose-response curves but am not sure >>> how to approach >>> more robust regression in R to get around the problem of the my error >>> showing increased variance with increasing dose. >>> >>> >>> >>> My understanding is that "rlm" or "lqs" would not be a good idea here. >>> 'Fairly new to regression work, so apologies if I'm missing something >>> obvious. >>> >>> >>> >>> >>> [[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> [email protected] mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide! >>> http://www.R-project.org/posting-guide.html >>> >>> >> >> ______________________________________________ >> [email protected] mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >> > > -- 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://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
