If that variable is a subject ID, and the data are repeated observations on the subjects, then you might be treading on thin ice here. A while back someone at NCI got a data set with two reps per subject, and he was able to modify the code so that the bootstrap is done on the subject basis, rather than observations. It's a bit of work trying to get a proximity matrix to make sense, though.
I really have no idea how to take care of repeated measures type data (i.e., accounting for intra-subject correlations) in a classification problem. I suppose one can formulate it as a GLMM. I guess it really depends on what you are looking for; i.e., what's the goal? I assume you want to predict something, but is that over all subjects, or subject-specific? I better stop here, as this is out of my league... Andy > From: David L. Van Brunt, Ph.D. [mailto:[EMAIL PROTECTED] > > Removing that first 39 level variable, the trees ran just > fine. I had also > taken the shorter categoricals (day of week, for example) and > read them in > as numerics. > > Still working on it. Need that 30 level puppy in there somehow, but it > really is not anything like a rank... It is a nominal variable. > > With numeric values, only assigning the outcome (last column) > to be a factor > using "as.factor()" it runs fine, and fast. > > I may be misusing this analysis. That first column is indeed > nominal, and I > was including it because the data within that name are > repeated observations > of that subject. But I suppose there's no guarantee that that > information > would be selected, so what does that do to the forest? Sigh. > I'm not much > of a lumberjack. Logistic regression is more my style, but > this is pretty > interesting stuff. > > If interested, here's a link to the data; > http://www.well-wired.com/reflibrary/uploads/1081216314.txt > > > > On 4/5/04 1:40, "[EMAIL PROTECTED]" > <[EMAIL PROTECTED]> wrote: > > > Alternatively, if you can arrive at a sensible ordering of > the levels > > you can declare them ordered factors and make the > computation feasible > > once again. > > > > Bill Venables. > > > > -----Original Message----- > > From: [EMAIL PROTECTED] > > [mailto:[EMAIL PROTECTED] On Behalf Of > Torsten Hothorn > > Sent: Monday, 5 April 2004 4:27 PM > > To: David L. Van Brunt, Ph.D. > > Cc: R-Help > > Subject: Re: [R] Can't seem to finish a randomForest.... > Just goes and > > goes! > > > > > > On Sun, 4 Apr 2004, David L. Van Brunt, Ph.D. wrote: > > > >> Playing with randomForest, samples run fine. But on real > data, no go. > >> > >> Here's the setup: OS X, same behavior whether I'm using > R-Aqua 1.8.1 > >> or the Fink compile-of-my-own with X-11, R version 1.8.1. > >> > >> This is on OS X 10.3 (aka "Panther"), G4 800Mhz with 512M physical > >> RAM. > >> > >> I have not altered the Startup options of R. > >> > >> Data set is read in from a text file with "read.table", and has 46 > >> variables and 1,855 cases. Trying the following: > >> > >> The DV is categorical, 0 or 1. Most of the IV's are either > continuous, > > > >> or correctly read in as factors. The largest factor has 30 > levels.... > >> Only the > > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > > > > This means: there are 2^(30-1) = 536.870.912 possible splits to be > > evaluated everytime this variable is picked up (minus > something due to > > empty levels). At least the last time I looked at the code, > randomForest > > used an exhaustive search over all possible splits. Try reducing the > > number of levels to something reasonable (or for a first > shot: remove > > this variable from the learning sample). > > > > Best, > > > > Torsten > > > > > >> DV seems to need identifying as a factor to force class trees over > >> regresssion: > >> > >>> Mydata$V46<-as.factor(Mydata$V46) > >>> > Myforest.rf<-randomForest(V46~.,data=Mydata,ntrees=100,mtry=7,proximi > >>> ties=FALSE > >> , importance=FALSE) > >> > >> 5 hours later, R.bin was still taking up 75% of my processor. When > >> I've tried this with larger data, I get errors referring > to the buffer > > > >> (sorry, not in front of me right now). > >> > >> Any ideas on this? The data don't seem horrifically large. > Seems like > >> there are a few options for setting memory size, but I'm not sure > >> which of them to try tweaking, or if that's even the issue. > >> > >> ______________________________________________ > >> [EMAIL PROTECTED] mailing list > >> https://www.stat.math.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide! > >> http://www.R-project.org/posting-guide.html > >> > >> > > > > ______________________________________________ > > [EMAIL PROTECTED] mailing list > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! > > http://www.R-project.org/posting-guide.html > > -- > David L. Van Brunt, Ph.D. > Outlier Consulting & Development > mailto: <[EMAIL PROTECTED]> > > > > ------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachments,...{{dropped}} ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
