Forgive me if I misunderstand your goals but I have no idea what you are
trying to determine or what your data is. I can say, however, that setting
mindev to 0 has always overfit data for me, and that you are more than
likely looking at a situation in which that 1 node tree is more accurate.
Also, if you look at ?cv.tree, the default function to use is
prune.tree(). Perhaps prune.tree() is trimming down to that terminal node?
If you want an alternative look at CART methods that may account for some
of your issues, I would recommend the packages 'rpart' and 'party', as
they may be more informative.
--------------------------------------
Jonathan P. Daily
Technician - USGS Leetown Science Center
11649 Leetown Road
Kearneysville WV, 25430
(304) 724-4480
"Is the room still a room when its empty? Does the room,
the thing itself have purpose? Or do we, what's the word... imbue it."
- Jubal Early, Firefly
From:
Shiyao Liu <lsy...@iastate.edu>
To:
r-help@r-project.org
Date:
11/03/2010 09:04 PM
Subject:
[R] cross-validation for choosing regression trees
Sent by:
r-help-boun...@r-project.org
Dear All,
We came across a problem when using the "tree" package to analyze our data
set.
First, in the "tree" function, if we use the default value "mindev=0.01",
the resulting regression tree has a single node. So, we set "mindev=0",
and
obtain a tree with 931 terminal nodes.
However, when we further use the "cv.tree" function to run a 10-fold
cross-validation, the error message is:
"Error in prune.tree(list(frame = list(var = 1L, n = 6676, dev =
3.28220789569792, : can not prune singlenode tree".
Is the "cv.tree" function respecting the mindev chosen in the tree
function
or what else might be wrong?
Thanks,
Shiyao
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