[R] Accessing terminal datasets in Ctree()

[Preetam Pal]

Again, really appreciate your help on this. Thanks, Achim.
-Preetam

On Tue, May 3, 2016 at 3:22 AM, Achim Zeileis <Achim.Zeileis at uibk.ac.at>
wrote:

> On Mon, 2 May 2016, Preetam Pal wrote:
>
> Great, thank you so much Achim.But one issue, in case I do not know how
>> many
>> terminal nodes would be there, what do I do? Note that I do not need the
>> datasets corresponding to the intermediate nodes only need the terminal
>> datasets.
>>
>
> With predict(ct, type = "node") you can set up a new variable, e.g.,
>
> iris$node <- factor(predict(ct, type = "node"))
>
> and then use this to obtain the subset corresponding to each of the
> terminal nodes.
>
>
> Regards,
>> Preetam
>>
>> On Tue, May 3, 2016 at 3:08 AM, Achim Zeileis <Achim.Zeileis at uibk.ac.at>
>> wrote:
>>       On Mon, 2 May 2016, Preetam Pal wrote:
>>
>>             Hi guys,
>>
>>             If I am applying ctree() on a data (specifying some
>>             control parameters like
>>             maxdepth), is there a way I can programmatically
>>             access the (smaller)
>>             datasets corresponding to the terminal nodes in the
>>             tree? Say, if there are
>>             7 terminal nodes, I need those 7 datasets (of
>>             course, I can look at the
>>             respective node-splitting attributes and write out a
>>             filtering function -
>>             but clearly too much to ask for if I have a large
>>             number of terminal
>>             nodes). Intention is to perform regression on each
>>             of these terminal
>>             datasets.
>>
>>
>>       If you use the "partykit" implementation you can do:
>>
>>       library("partykit")
>>       ct <- ctree(Species ~ ., data = iris)
>>       data_party(ct, id = 6)
>>
>>       to obtain the data associated with node 6 for example. You can
>>       also use ct[6] to obtain the subtree and ct[6]$data for its
>>       associated data.
>>
>>       For setting up a factor with the terminal node IDs, you can also
>>       use predict(ct, type = "node") and then use that in lm() etc.
>>
>>       Finally, note that there is also lmtree() and glmtree() for
>>       trees with (generalized) linear models in their nodes.
>>
>>             Regards,
>>             Preetam
>>
>>             --
>>             Preetam Pal
>>             (+91)-9432212774
>>             M-Stat 2nd Year,
>>                      Room No. N-114
>>             Statistics Division,
>>                        C.V.Raman
>>             Hall
>>             Indian Statistical Institute,
>>                      B.H.O.S.
>>             Kolkata.
>>
>>                     [[alternative HTML version deleted]]
>>
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>>
>>
>>
>>
>> --
>> Preetam Pal
>> (+91)-9432212774
>> M-Stat 2nd Year,                                             Room No.
>> N-114
>> Statistics Division,                                           C.V.Raman
>> HallIndian Statistical Institute,                                 B.H.O.S.
>> Kolkata.
>>
>>


-- 
Preetam Pal
(+91)-9432212774
M-Stat 2nd Year,                                             Room No. N-114
Statistics Division,                                           C.V.Raman
Hall
Indian Statistical Institute,                                 B.H.O.S.
Kolkata.

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