[R] Is there a "Mixed effect model" for regression/classification trees?

[Achim Zeileis]

On Sun, 11 Jul 2010, Tal Galili wrote:

> Hello all,
>
> This is more a statistical question then an R question, but I am sure it
> will have an R interpretation to it.
>
> If I wish to predict an outcome based on some potential features, I could
> (in some cases) use either regression or regression-tree.
> However, if my observations are divided to groups (for example by
> "subject"), I might then want to model that using a random effect for the
> "subject" and a fixed effect for the other features I wish to model for the
> prediction.
> My question is what (if exist) is the parallel of this in regression trees ?
> Is it simply like adding the "subject" classifier to the tree? or is this
> leading to a different model scheme all together? (and if so - what is it)

We had thought about doing something in this direction in the model-based 
recursive partitioning framework (MOB) in the "party" package but never 
pulled it off.

However, I've seen various other ideas at conferences for trees with 
random intercepts etc.

One CRAN package that provides infracstructure for this is "REEMtree" 
which is probably worth a look.

hth,
Z

>
> Thanks,
> Tal
>
>
>
>
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