similar to: randomForest: too many element specified?

I getting "Error in matrix(0, n, n) : too many elements specified" while building randomForest model, which looks like memory allocation error. Software versions are: randomForest 4.5-25, R version 2.7.1 Dataset is big (~90K rows, ~200 columns), but this is on a big machine ( ~120G RAM) and I call randomForest like this: randomForest(x,y) i.e. in supervised mode and not requesting

Hi, I attempted to run the randomForest() function on a dataset without predefined classes. According to the manual, running randomForest without a response variable/class labels should result in the function assuming you are running in unsupervised mode. In this case, I understand that my data is all assigned to one class whereas a second synthetic class is made up, which is assigned

Hi I am new to R, randomForest and I have read about how to use it in your old mails. I have also run the predictions examples from CRAN. But I still don't understand how to use it right. The thing that I don't understand is how to run the result from the randomForest on one line (post) with newdata to get a good guess. What I mean is if I put in a new observation of iris how do I

Dear all, I am trying to calculate the proximity matrix for a data set with 16 variables and 6804 observations using random forests. I have a Pentium 4, 3.00GHz processor with 1 GB of RAM. When I use the command randomForest(data.scale,proximity=T) I get the warning message Error: cannot allocate vector of size 361675 kb Is this because I have reached the limit of what my computer is

While trying to train randomForest with my dataset, I am ending up with the following error Error in randomForest.default(datatrain, classtrain) : length of response must be the same as predictors My data looks like: A,B,C,D,Class 1,2,1,2,cl1 1,2,1,2,cl1 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,12,3,cl2 3,2,1,2,cl2 Actual dataset has around 4000

Hi Jan-Paul, You definitely want to be careful with na.omit in randomForest -- that wipes out any row with even one NA. If NAs are sprawled throughout your dataset, na.omit might end up killing a lot of rows. Here's my usual MO for missing values: 1) "impute" in Hmisc fills in gaps with the mean, median, most common value, etc. 2) rfImpute: fits a forest on the rows available and

The link from http://cran.r-project.org/src/contrib/PACKAGES.html#randomForest seems to be broken. To get the file try http://cran.r-project.org/src/contrib/randomForest_3.3-4.tar.gz instead. > -----Original Message----- > From: Liaw, Andy [mailto:andy_liaw at merck.com] > Sent: Thursday, September 26, 2002 8:08 PM > To: 'r-announce at stat.math.ethz.ch' > Subject:

The `1' and `2' columns are the error rates within those classes. E.g., the last row of the `1' column should correspond to the class.error for "-", and the last row of the `2' column to the class.error for "+". (I would have thought that that should be fairly obvious, but I guess not. It mimics what Breiman and Cutler's Fortran code does.) I suspect

A few days ago I asked whether there were options other than na.action=na.fail for the R port of Breiman?s randomForest; the function?s help page did not say anything about other options. I have since discovered that a pdf document called ?The randomForest Package? and made available by Andy Liaw (who made the tool available in R---thank you) does discuss an option. It is an implementation of

I used the following command to train a randomForest model train.rf <- randomForest(grp ~ ., data=tr, ntree=100, mtry=50) My question is how to save the trained model so that it can be loaded later for testing new samples? Thanks, Luk --------------------------------- [[alternative HTML version deleted]]

Is there a wavelet toolbox in R available that can be compared to the Matlab toolbox for wavelets. Does this toolbox offer the possibility to calculated approximations with different wavelets such as Daubechies 2, Daubechies 3, etc, Symmlet 3, Symmlets 5, etc, Coiflets and so on. I have tried to proceed in this way but I was only able to use one sort of wavelets with a pre-fixed order (i.e.

> From: luk > > I got the following results when I run radomForest with below > commands: > > qair <- read.table("train10.dat", header = T) > oz.rf <- randomForest(LESION ~ ., data = qair, ntree = 220, > importance = TRUE) > print(oz.rf) > > Call: > randomForest.formula(x = LESION ~ ., data = qair, ntree = > 220, importance =

Greetings, All! I'm facing a problem with one particular directory in a mailbox. The exact error line is: Apr 7 12:00:21 mxs dovecot: imap(vera): Error: rename(/home/vera/.mail/INBOX/???? ??????????/new/1678353168.M710968P25673.mxs.ads.ccenter.lan,S=5442,W=5513:2,ST, /home/vera/.mail/INBOX/???? ??????????/cur/1678353168.M710968P25673.mxs.ads.ccenter.lan,S=5442,W=5513:2,ST) failed: Too many

In R 3.5 and later you should not need to gc() -- that should happen automatically within the connections code. Nevertheless, I would recommend redesigning your approach to avoid hanging onto open file connections as these are a scarce resource. You can keep around your temporary files without having them open and only open/close them on access, with the close run in an on.exit or a

Mathew Brown Institute of Bioclimatology University of G?ttingen B?sgenweg 2 37077 G?ttingen, Germany t: +49 551 39 9359 mathew.brown at forst.uni-goettingen.de On 9/24/2011 6:00 PM, r-help-request at r-project.org wrote: > Send R-help mailing list submissions to > r-help at r-project.org > > To subscribe or unsubscribe via the World Wide Web, visit >

Hi all, We're using Rserve and RandomForest to do classification from within a Java program. The total is about 4 lines of R code: library('randomForest') x y future fit<-randomForest(x,y,no.action=na.roughfix,importance=T,proximity=T) p<-predict(fit, future) What is very frustrating is that we have tried this two different ways (both work in R): 1. Load x, y, and future

Hi I am trying to use randomForest for classification. I am using this code: > set.seed(71) > rf.model <- randomForest(similarity ~ ., data=set1[1:100,], importance=TRUE, proximity=TRUE) Warning message: The response has five or fewer unique values. Are you sure you want to do regression? in: randomForest.default(m, y, ...) > rf.model Call: randomForest(x = similarity ~ .,

Dear all: I¡¯m trying to get unbiased feature importance of my data via package ¡°party¡±, which contains 1-5 integer value, and a few numeric values attributes. The class label is 1-5 integer value as well. In total I have 20 features with 1100 observations. I checked the type my data in R using class(my_data_cell), no factor has been observed. I received a commond error like others did

Dear all, I run the 64 bit version of R under ubuntu 8.10. I have a dual core machine with 5 GB RAM. Further, I increased virtual memory with a swapfile to 90 GB. If I run a program which uses large matrixes, I get the following error message: "Error in matrix: too many elements specified". Is there a possibility to increase the possible size of a matrix? Many thanks and

hello, I want to use randomForest to classify a matrix which is 331030?42,the last column is class signal.I use ? Memebers.rf<-randomForest(class~.,data=Memebers,proximity=TRUE,mtry=6,ntree=200) which told me" the error is matrix(0,n,n) set too elements" then I use: Memebers.rf<-randomForest(class~.,data=Memebers,importance=TRUE,proximity=TRUE) which told me"the error is