[R] 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" <andy_liaw at merck.com> 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]]
>>>
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>>
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>

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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