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.  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:  brian_cade@usgs.gov
tel:  970 226-9326



Kjetil Brinchmann Halvorsen <kjetilbrinchmannhalvorsen@gmail.com> 
Sent by: r-help-bounces@stat.math.ethz.ch
02/21/2006 03:31 PM
Please respond to
KjetilBrinchmannHalvorsen@gmail.com


To
Quin Wills <quin.wills@googlemail.com>
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.
> 
> 
> 
> 
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