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

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

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

Kjetil Halvorsen
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
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Re: How to get around heteroscedasticity with non-linear least squares in R?

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.  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]>
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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
>

______________________________________________
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