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$node <- factor(predict(ct, type = "node"))
and then use this to obtain the subset corresponding to each of the
terminal nodes.
Regards,
Preetam
On Tue, May 3, 2016 at 3:08 AM, Achim Zeileis <achim.zeil...@uibk.ac.at>
wrote:
On Mon, 2 May 2016, Preetam Pal wrote:
Hi guys,
If I am applying ctree() on a data (specifying some
control parameters like
maxdepth), is there a way I can programmatically
access the (smaller)
datasets corresponding to the terminal nodes in the
tree? Say, if there are
7 terminal nodes, I need those 7 datasets (of
course, I can look at the
respective node-splitting attributes and write out a
filtering function -
but clearly too much to ask for if I have a large
number of terminal
nodes). Intention is to perform regression on each
of these terminal
datasets.
If you use the "partykit" implementation you can do:
library("partykit")
ct <- ctree(Species ~ ., data = iris)
data_party(ct, id = 6)
to obtain the data associated with node 6 for example. You can
also use ct[6] to obtain the subtree and ct[6]$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.
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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.
______________________________________________
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