R help - Feb 2006 - How to get around heteroscedasticity with non-linear least squares in R?

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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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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Hi Quin,

the package 'drc' on CRAN deals with modelling dose-response curves.

Moreover it allows adjustment for heterogeneity by means of 


  transformation (Box-Cox transformation)

  modelling the variance as a power of the mean.


See the package documentation for more features.


Christian