Dear R users,

In the hier.part package, hierarchical partitioning is built 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. In this context, the joint and independent effect from each factor
can be evaluated throughout the R² calculation (adjusted). My two questions
are:

1. Conceptually and from a statistical point of view, are there any problems
to adapt the philosophy of hierarchical partitioning to a GAM (generalized
additive model) framework when some quantitative predictors are supposed to
have a non-linear effect with the response variable?

2. Let's say that the total variance of a single continuous variable can be
explained by two qualitative factors (X1 and X2). I wonder if this total R²
value resulting from the hier.part analysis (total=joint+independent value),
and calculated individually for X1 and X2, can be discussed in terms of the
inter-group variance (i.e. something like the variance between the means of
each modalities of factors). If so, can the unexplained variation from the
analysis (1- total R²) be associated to the intra-group variance (e.g.
something like the variance within each modalities of factors)?
.
Thanks in advance for your help,

Clement

-- 
Clement Tisseuil - PhD student

Laboratoire  "Evolution et Diversité Biologique" (EDB)
UMR 5174 - Université Paul Sabatier CNRS
118 route de Narbonne, Bât 4R3, Porte 112
31062 Toulouse Cedex 9 - France
Phone : +33 5 61 55 67 35
Fax: +33 5 61 55 67 28
webpage: http://www.clement-tisseuil.eu

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