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

From: Liaw, Andy <andy_liaw_at_merck.com>
Date: Wed 22 Feb 2006 - 12:17:44 EST


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: 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.
> >
> >
> >
> >
> > [[alternative HTML version deleted]]
> >
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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 Received on Wed Feb 22 19:01:19 2006

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