On Tue, 9 Jan 2007, Bálint Czúcz wrote:
There is an improved version of the original random forest algorithm
available in the "party" package (you can find some additional
information on the details here:
http://www.stat.uni-muenchen.de/sfb386/papers/dsp/paper490.pdf ).
I do not know whether it yields a solution to your problem about
missing data, but maybe it's a check worth...
yes, `cforest()' is able to deal with missing values. More specifically,
the implementation is based on conditional trees (`ctree()') which are
able to set up surrogate splits.
Torsten
Best regards:
Bálint
On 1/4/07, Darin A. England <[EMAIL PROTECTED]> wrote:
Does anyone know a reason why, in principle, a call to randomForest
cannot accept a data frame with missing predictor values? If each
individual tree is built using CART, then it seems like this
should be possible. (I understand that one may impute missing values
using rfImpute or some other method, but I would like to avoid doing
that.)
If this functionality were available, then when the trees are being
constructed and when subsequent data are put through the forest, one
would also specify an argument for the use of surrogate rules, just
like in rpart.
I realize this question is very specific to randomForest, as opposed
to R in general, but any comments are appreciated. I suppose I am
looking for someone to say "It's not appropriate, and here's why
..." or "Good idea. Please implement and post your code."
Thanks,
Darin England, Senior Scientist
Ingenix
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