Thanks for the pointer!! Can't believe you got back to me so quickly on a Sunday evening. I'll give that a shot and let you know how it goes.
On 4/4/04 19:07, "Liaw, Andy" <[EMAIL PROTECTED]> wrote: > When you have fairly large data, _do not use the formula interface_, as a > couple of copies of the data would be made. Try simply: > > Myforest.rf <- randomForest(Mydata[, -46], Mydata[,46], > ntrees=100, mtry=7) > > [Note that you don't need to set proximity (not proximities) or importance > to FALSE, as that's the default already.] > > You might also want to use do.trace=1 to see if trees are actually being > grown (assuming there's no output buffering as in Rgui on Windows, otherwise > you'll probably want to turn that off). > > I had run randomForest on data set much larger than that, without problem, > so I don't imagine your data would be `difficult'. (I have not used the > Mac, though.) > > Andy > >> From: David L. Van Brunt, Ph.D. >> >> 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 >> 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 > ,proximities=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 >> >> > > > ------------------------------------------------------------------------------ > Notice: This e-mail message, together with any attachments, contains > information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New > Jersey, USA 08889), and/or its affiliates (which may be known outside the > United States as Merck Frosst, Merck Sharp & Dohme or MSD and in Japan, as > Banyu) that may be confidential, proprietary copyrighted and/or legally > privileged. It is intended solely for the use of the individual or entity > named on this message. If you are not the intended recipient, and have > received this message in error, please notify us immediately by reply e-mail > and then delete it from your system. > ------------------------------------------------------------------------------ -- David L. Van Brunt, Ph.D. Outlier Consulting & Development mailto: <[EMAIL PROTECTED]> ______________________________________________ [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
