R help - Nov 2011 - linear against nonlinear alternatives - quantile regression

Dear all,

I would like to know whether any specification test for linear against nonlinear
model hypothesis has been implemented in R using the quantreg package.

I could read papers concerning this issue, but they haven't been implemented
at R. As far as I know, we only have two specification tests in this line:
anova.rq and Khmaladze.test. The first one test equality and significance of the
slopes across quantiles and the latter one test if the linear specification is
model of location or location and scale shift.

Do you have any suggestion?

Thanks a lot!

Best regards,

Julia
 		 	   		  
	[[alternative HTML version deleted]]

Just to address a piece of this - in the case in which you are currently
focusing on only one quantile, the rms package can help by fitting
restricted cubic splines for covariate effects, and then run anova to test
for nonlinearity (sometimes a dubious practice because if you then remove
nonlinear terms you are mildly cheating).

require(rms)
f <- Rq(y ~ x1 + rcs(x2,4), tau=.25)
anova(f)  # tests associations and nonlinearity of x2

Frank

Julia Lira wrote:
> 
> Dear all,
> 
> I would like to know whether any specification test for linear against
> nonlinear model hypothesis has been implemented in R using the quantreg
> package. 
> 
> I could read papers concerning this issue, but they haven't been
> implemented at R. As far as I know, we only have two specification tests
> in this line: anova.rq and Khmaladze.test. The first one test equality and
> significance of the slopes across quantiles and the latter one test if the
> linear specification is model of location or location and scale shift. 
> 
> Do you have any suggestion?
> 
> Thanks a lot!
> 
> Best regards,
> 
> Julia
>  		 	   		  
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help@ mailing list
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
> 

-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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I'm just curious if Professor Koenker responded to this (e.g. in an
individual email or otherwise), as I would be interested in the answer.

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