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

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

Liaw, Andy
Your understanding isn't similar to mine.  Mine says robust/resistant
methods are for data with heavy tails, not heteroscedasticity.  The common
ways to approach heteroscedasticity are transformation and weighting.  The
first is easy and usually quite effective for dose-response data.  The
second is not much harder.  Both can be done in R with nls().

Andy

From: Quin Wills

>
> 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.  
>
>  
>
> 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]]
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>
>

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

Peter Dalgaard
"Liaw, Andy" <[hidden email]> writes:

> Your understanding isn't similar to mine.  Mine says robust/resistant
> methods are for data with heavy tails, not heteroscedasticity.  The common
> ways to approach heteroscedasticity are transformation and weighting.  The
> first is easy and usually quite effective for dose-response data.  The
> second is not much harder.  Both can be done in R with nls().

And there is gnls() which allows direct modelling of the variance.

        -p
 

> Andy
>
> From: Quin Wills
> >
> > 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.  
> >
> >  
> >
> > 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]]
> >
> > ______________________________________________
> > [hidden email] mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
> > http://www.R-project.org/posting-guide.html
> >
> >
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
>

--
   O__  ---- Peter Dalgaard             Ă˜ster Farimagsgade 5, Entr.B
  c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
 (*) \(*) -- University of Copenhagen   Denmark          Ph:  (+45) 35327918
~~~~~~~~~~ - ([hidden email])                  FAX: (+45) 35327907

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

Prof Brian Ripley
On Tue, 21 Feb 2006, Peter Dalgaard wrote:

> "Liaw, Andy" <[hidden email]> writes:
>
>> Your understanding isn't similar to mine.  Mine says robust/resistant
>> methods are for data with heavy tails, not heteroscedasticity.  The common
>> ways to approach heteroscedasticity are transformation and weighting.  The
>> first is easy and usually quite effective for dose-response data.  The
>> second is not much harder.  Both can be done in R with nls().
>
> And there is gnls() which allows direct modelling of the variance.

in package nlme, BTW.

R-devel allows weights in nls, which makes it easier for those most
familiar with that function.

>
>        -p
>
>> Andy
>>
>> From: Quin Wills
>>>
>>> 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.
>>>
>>>
>>>
>>> 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]]
>>>
>>> ______________________________________________
>>> [hidden email] mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide!
>>> http://www.R-project.org/posting-guide.html
>>>
>>>
>>
>> ______________________________________________
>> [hidden email] mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
>>
>
>

--
Brian D. Ripley,                  [hidden email]
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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

Liaw, Andy
In reply to this post by Liaw, Andy
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:  [hidden email]
> tel:  970 226-9326
>
>
>
> Kjetil Brinchmann Halvorsen <[hidden email]>
> Sent by: [hidden email]
> 02/21/2006 03:31 PM
> Please respond to
> [hidden email]
>
>
> To
> Quin Wills <[hidden email]>
> cc
> [hidden email]
> 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]]
> >
> > ______________________________________________
> > [hidden email] mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
> >
>
> ______________________________________________
> [hidden email] 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]]
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>
>

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

Quin Wills
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:[hidden email]]
Sent: 22 February 2006 01:18
To: 'Brian S Cade'; [hidden email]
Cc: Quin Wills; [hidden email]; [hidden email]
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:  [hidden email]
> tel:  970 226-9326
>
>
>
> Kjetil Brinchmann Halvorsen <[hidden email]>
> Sent by: [hidden email]
> 02/21/2006 03:31 PM
> Please respond to
> [hidden email]
>
>
> To
> Quin Wills <[hidden email]>
> cc
> [hidden email]
> 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]]
> >
> > ______________________________________________
> > [hidden email] mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
> >
>
> ______________________________________________
> [hidden email] 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]]
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>
>


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