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
Quantile GAM?
3 messages · Jonathan Greenberg, Gavin Simpson, Mark Difford
On Fri, 2009-05-29 at 16:51 -0700, Jonathan Greenberg wrote:
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!
If you're just after the AM bit of GAM, then yes, I think quantreg contains functions to do as you wish. See ?rqss and ?qss for models that allow smoothing splines in quantile regression. Alternatively, I think package VGAM may also contain something of use. HTH G
--j
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
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Hi Jonathan,
I was wondering if anyone had suggestions on how to implement a GAM in a quantile fashion?
Take a look at the gamlss package. Regards, Mark.
Jonathan Greenberg-2 wrote:
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
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
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