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

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.

 


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"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.

       -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.




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