[R] Quantile regression questions

[roger koenker]

Brian,

It is hard to say at this level of resolution of the question, but it  
would seem that you might
be able to start by considering each sample vector as as repeated  
measurement of the
fiber length -- so 12 obs in the first 1/16th bin, 235 in the next  
and so forth, all associated
with some vector of covariates representing location, variety, etc,  
then the conventional
quantile regression would serve to estimate a conditional quantile  
function for fiber length
for each possible covariate setting --- obviously this would require  
some model for the
way that the covariate effects fit together, linearity,  possible  
interactions, etc etc, and it
would also presume that it made sense to treat the vector of  
responses as independent
measurements.  Building in possible dependence involves some new  
challenges, but
there is some recent experience with inferential methods for  
microarrays that have
incorporated these effects.

I'd be happy to hear more about the data and possible models, but  
this should be
routed privately since the topic is rather too specialized for R-help.


url:    www.econ.uiuc.edu/~roger            Roger Koenker
email    rkoenker at uiuc.edu            Department of Economics
vox:     217-333-4558                University of Illinois
fax:       217-244-6678                Champaign, IL 61820


On Oct 26, 2006, at 7:20 AM, Brian Gardunia wrote:

> I am relatively new to R, but am intrigued by its flexibility.  I  
> am interested in quantile regression and quantile estimation as  
> regards to cotton fiber length distributions.  The length  
> distribution affects spinning and weaving properties, so it is  
> desirable to select for certain distribution types.  The AFIS fiber  
> testing machinery outputs a vector for each sample of type c(12,  
> 235, 355, . . . n) with the number of fibers in n=40 1/16 inch  
> length categories.  My question is what would be the best way to  
> convert the raw output to quantiles and whether it would be  
> appropriate to use quantile regression to look at whether location,  
> variety, replication, etc. modify the length distribution.
>
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