Hi Birgit,
I'm not sure that I understand your question. I'll try to answer
anyways. Regression trees and therefore also RandomForests are invariant
to monotonic transformations in the independent variables. There are no
distributional assumptions for the independent variables. The dependent
variable, however, is used to calculate the variances within the two
groups of cases that result from a split. Therefore, it would make sense
to have the dependent variable follow the typical distributional
requirements of least-squares driven models such as homoscedasity,
symmetrical distribution etc. For count data a square root
transformation is often appropriate.
HTH
Volker
Birgit Lemcke wrote:
<div class="moz-text-flowed" style="font-family: -moz-fixed">Hello
R-user!
I am running R 2.7.0 on a Power Book (Tiger). (I am still R and
statistics beginner)
I try to find the most important variables to divide my dataset as
given in a categorical variable using randomForest.
Is randomForest() able to deal with count data?
Or is there no difference because only the ranks are used in the trees?
Thanks in advance
Birgit
Birgit Lemcke
Institut für Systematische Botanik
Zollikerstrasse 107
CH-8008 Zürich
Switzerland
Ph: +41 (0)44 634 8351
[EMAIL PROTECTED]
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</div>
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