It would be good to tell us of the frequency of observations in each
category of Y, and the number of continuous X's. Recursive
partitioning will require perhaps 50,000 observations in the less
frequent Y category for its structure and predicted values to
validate, depending on X and the signal:noise ratio. Hence the use of
combinations of trees nowadays as opposed to single trees. Or
logistic regression.
Frank
Frank E Harrell Jr Professor and Chairman School of Medicine
Department of Biostatistics Vanderbilt University
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).
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.
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
-----
------------------------
Kay Cichini
Postgraduate student
Institute of Botany
Univ. of Innsbruck
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