Dear R community,
The hier.part package applies hierarchical partitioning based upon a GLM
(generalized linear model) framework to assess the independent and joint
effect from a set of predictors onto a single quantitative response
variable. Conceptually and from a statistical point of view, are there any
problems to adapt the philosophy of hierarchical partitioning under a GAM
(generalized additive model) framework, or more generally to any other
non-linear modelling framework such as neural networks or regression trees?
Best regards
Clement
[[alternative HTML version deleted]]
______________________________________________
R-help@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.
