Hello Achim,

Thank you very much,  this looks exactly like what I was looking for.
(p.s: Their presentation
here<http://pages.stern.nyu.edu/~rsela/REEMtree/REEMpresentation.pdf>
Is
most helpful)


Best,
Tal


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Contact me: Tal.Galili@gmail.com |  972-52-7275845
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On Mon, Jul 12, 2010 at 2:03 AM, Achim Zeileis <Achim.Zeileis@uibk.ac.at>wrote:

> 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
>>
>>
>>
>>
>> ----------------Contact
>> Details:-------------------------------------------------------
>> Contact me: Tal.Galili@gmail.com |  972-52-7275845
>> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
>> www.r-statistics.com (English)
>>
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>>
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>>

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