There are possibilities with rqss() as someone else mentioned.  But you 
can also conduct a lot of useful modeling just by using b-splines within 
the the rq function - something like
my.result <- rq(y ~ bs(x,degree=3)), where bs() is the b-spline function 
from the splines package.  You get to specifiy the degree of polynomial 
and number and location of knots.

Brian


Brian S. Cade, PhD

U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO  80526-8818

email:  brian_cade@usgs.gov
tel:  970 226-9326



From:
Jonathan Greenberg <greenberg@ucdavis.edu>
To:
"r-help@r-project.org" <r-help@r-project.org>
Date:
05/29/2009 05:55 PM
Subject:
[R] Quantile GAM?
Sent by:
r-help-bounces@r-project.org



R-ers:

    I was wondering if anyone had suggestions on how to implement a GAM 
in a quantile fashion?  I'm trying to derive a model of a "hull" of 
points which are likely to require higher-order polynomial fitting (e.g. 
splines)-- would quantreg be sufficient, if the response and predictors 
are all continuous?  Thanks!

--j

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