What happens if you treat the DV as continuous?
There is also work by Marilyn Ritchie at vanderbilt (I assume genetics) and
Rob Culverhouse (cced on this) in my group that uses a partitioning algorithm
(not tree but perhaps usable) you could try.
Bill Shannon
Peter Flom <[EMAIL PROTECTED]> wrote:
I would be interested in any references anyone can supply on ordinal
trees, either alone or in combination with ordinal logistic regression.
Here is a brief outline of what I am trying to do:
We have a DV that is ordinal - level of dementia in the elderly. In our data
set, it has six levels, with more people in the middle levels than the extreme
ones. Total N is about 1,000. We have a great many potential IVs (almost
2000) but many of these are highly correlated, and some are more likely to be
related to the DV than others. I've done a lot of data reduction, getting it
down to about 100 IVs.
The problem is that the relationship between the DV and the IVs is different at
different levels of the DV. For instance, some IVs are similar at DV = 1, 2,
or 3 but then jump and are similar at 4,5, or 6. Others show different
patterns.
I've tried a few different things. One that seems to show promise is first
doing a tree of 1,2,3 vs. 4,5,6 then doing trees among 1,2,3 and 4,5,6
separately. But this is problematic because the first tree, while it works
fairly well, does not work nearly perfectly.
I am using CART for the tree analysis, and have SAS and R for other statistical
analyses.
So, before I reinvent the wheel, I wanted to ask if anyone has seen something
like this before.
Thanks in advance
Peter
Peter L. Flom, PhD
Brainscope, Inc.
212 263 7863 (MTW)
212 845 4485 (Th)
917 488 7176 (F)
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