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September 2007

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Subject:
From:
Art Kendall <[log in to unmask]>
Reply To:
Classification, clustering, and phylogeny estimation
Date:
Wed, 12 Sep 2007 19:42:49 -0400
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Did you check out the CATREG and CATPCA procedures in the SPSS 
CATEGORIES.  The people at Leiden designed it.  I haven't had a chance 
to try it myself, but have heard noting but positive things about it.

*IFF* I understand correctly, you can have it test differences for fit 
using nominal, ordinal, and interval assumptions.

Art Kendall
Social Research Consultants

Peter Flom 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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