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... 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 > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.