How to get around heteroscedasticity with non-linear leas t squares in R?

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 at usgs.gov
tel:  970 226-9326



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


To
Quin Wills <quin.wills at googlemail.com>
cc
r-help at 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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