On Fri, 20 Aug 2010, Kay Cichini wrote:
hello gavin & achim,
thanks for responding.
by logistic regression tree i meant a regression tree for a binary
response variable. but as you say i could also use a classification tree
- in my case with only two outcomes.
i'm not aware if there are substantial differences to expect for the two
approaches (logistic regression tree vs. classification tree with two
outcomes).
I don't think that there is a universally accepted terminology for this.
Classification tree typically pertains to categorical responses
(independet of the number of categories, i.e., also for binary responses).
Logistic regression tree is (to the best of my knowledge) not typically
used as a term for binary classification trees.
Technical excursion:
However, logistic regression tree may mean a specific algorithm (LOTUS -
LOgistic regression Tree with Unbiased Splits) developed by Kin-Yee Chan
and Wei-Yin Loh. This algorithms shares various ideas with the LMT
(Logistic Model Trees) algorithm developed by Niels Landwehr with
co-authors (available in R through "RWeka") and the MOB (MOdel-Based
partitioning) algorithm when employed with binary GLMs (as available in
the "party" package).
as i'm new to trees / boosting / etc. i also might be advised to use the
more comprehensible method / a function which argumentation is
understood without having to climb a steep learning ledder,
respectively. at the moment i don't know which this would be.
Trees may be a good starting point. As I wrote to you off-list: Feel free
to drop by my office if you want to chat about this.
Best,
Z
regarding the meaning of absences at stands: as these species are
frequent in the area and hence there is no limitation by propagules i
guess absence is really due to unfavourable conditions.
thanks a lot,
kay
-----
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Kay Cichini
Postgraduate student
Institute of Botany
Univ. of Innsbruck
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