Thank you all, this has been a great help (including the methodological advice). Very interesting - I'll be sure to read the lecture.
Quin -----Original Message----- From: Liaw, Andy [mailto:[EMAIL PROTECTED] Sent: 22 February 2006 01:18 To: 'Brian S Cade'; [EMAIL PROTECTED] Cc: Quin Wills; r-help@stat.math.ethz.ch; [EMAIL PROTECTED] Subject: RE: [R] How to get around heteroscedasticity with non-linear leas t squares in R? From: Brian S Cade > > Instead of thinking that the heteroscedasticity is a nuisance and > something to "get around", i.e, just wanting weighted > estimates of the > mean function, you might want to think about what > heteroscedasticity is > telling you and estimate some other quantities. Indeed! See Prof. Carroll's 2002 Fisher Lecture: http://www.stat.tamu.edu/ftp/pub/rjcarroll/2003.papers.directory/published_F isher_Lecture.pdf (There's Powerpoint file on his web page, too.) Andy > Heteroscedasticity is > telling you that the conditional distributions don't change > at a constant > rate across all portions of the distribution (think > percentiles or more > generally quantiles) and, therefore, a function for the mean > (no matter > how precisely estimated) cannot tell you all there is to know > about your > dose-response relation. Why not go after estimating the conditional > quantile functions directly with nonlinear quantile > regression, function > nlrq() in the quantreg package? > > Brian > > Brian S. Cade > > U. S. Geological Survey > Fort Collins Science Center > 2150 Centre Ave., Bldg. C > Fort Collins, CO 80526-8818 > > email: [EMAIL PROTECTED] > tel: 970 226-9326 > > > > Kjetil Brinchmann Halvorsen <[EMAIL PROTECTED]> > Sent by: [EMAIL PROTECTED] > 02/21/2006 03:31 PM > Please respond to > [EMAIL PROTECTED] > > > To > Quin Wills <[EMAIL PROTECTED]> > cc > r-help@stat.math.ethz.ch > Subject > Re: [R] How to get around heteroscedasticity with non-linear > least squares > in R? > > > > > > > Quin Wills wrote: > > 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. > > > > package "sfsmisc" has rnls (robust nls) > which might be of use. > > Kjetil > > > > > > > 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]] > > > > ______________________________________________ > > R-help@stat.math.ethz.ch mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > ---------------------------------------------------------------------------- -- Notice: This e-mail message, together with any attachments,...{{dropped}} ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
