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_c...@usgs.gov
tel: 970 226-9326
From:
Jonathan Greenberg <greenb...@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-boun...@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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