How to get around heteroscedasticity with non-linear leas t squares in R?
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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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) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595