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]] ______________________________________________ Rfirstname.lastname@example.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.