Re: [R] How to get around heteroscedasticity with non-linear least squares in R?

From: Brian S Cade <brian_cade_at_usgs.gov>
Date: Wed 22 Feb 2006 - 10:34:34 EST


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>
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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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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Feb 22 10:39:27 2006

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