[R] Deviance function in regression trees

[Prof Brian Ripley]

The short answer is that a Poisson distribution is a discrete 
distribution: if that is appropriate to your data the rpart function (in 
the package of that name) has a suitable option.

On Mon, 28 Aug 2006, Solomon Dobrowski wrote:

> Hello all. I have heard over and over that CART and its various tree-like
> brethren are "non-parametric" techniques.  When I read the chapter in
> Chambers and Hastie on tree-based models it states that tree-based models
> can be generalized (GTMs) in a manner similar to GLMs by specifying a
> different deviance function to distributions other than the gaussian error
> distribution ( section 9.4.3).  I have an application in which the response
> variable is a continuous variable representing tree counts within a unit
> area and thus would be best described by a poisson distribution. The error
> distribution for this data is not gaussian. If this is the case, will the
> gaussian error distribution used in most regression tree packages, be
> appropriate? Are there ways to specify the error distribution in R or Should
> I log transform the response variable?  If the specification of error
> distribution in regression trees is important, than are these techniques
> truly " non-parametric". Thanks for your inputs. 
> 
> 
> Solomon Dobrowski
> Tahoe Environmental Research Center (TERC)
> John Muir Institute of the Environment
> University of California, Davis
> 530 754 9354
> 
> 
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help at stat.math.ethz.ch 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.
> 

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595