similar to: NA in R package randomForest

Hi all, I am using the package "random forest" for random forest predictions. I like the package. However, I have fairly large data sets, and it can often take *hours* just to go through the "na.roughfix" call, which simply goes through and cleans up any NA values to either the median (numerical data) or the most frequent occurrence (factors). I am going to start

I am having trouble with the rfImpute function in the randomForest package. Here is a sample... clunk.roughfix<-na.roughfix(clunk) > > clunk.impute<-rfImpute(CONVERT~.,data=clunk) ntree OOB 1 2 300: 26.80% 3.83% 85.37% ntree OOB 1 2 300: 18.56% 5.74% 51.22% Error in randomForest.default(xf, y, ntree = ntree, ..., do.trace = ntree, : NA not

So I have a very big matrix of about 900 by 400 and there are a couple of NA in the list. I have used the following functions to impute the missing data data(pc) pc.na<-pc pc.roughfix <- na.roughfix(pc.na) pc.narf <- randomForest(pc.na, na.action=na.roughfix) yet it does not replace the NA in the list. Presently I want to replace the NA with maybe the mean of the rows or columns or

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

In trying to execute this line in R (Version 1.7.1 (2003-06-16), under windows XP pro), with the randomForest library (about two weeks old) loaded, the program crashed: bost4rf <- rfImpute(TargetDensity~.,data=bost4rf0) Specifically, an XP dialog box popped up, saying ?R for windows GUI front-end has encountered a problem and needs to close.? That was the dialog saying asking whether I

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

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

I've built two RF objects (RF1 and RF2) and have tried to combine them, but I get the following error: Error in rf$votes + ifelse(is.na(rflist[[i]]$votes), 0, rflist[[i]]$votes) : non-conformable arrays In addition: Warning message: In rf$oob.times + rflist[[i]]$oob.times : longer object length is not a multiple of shorter object length Both RF models use the same variables, although

Hello, I have a large data set 320.000 rows and 1000 columns. All the data has the values 0,1,2. I wrote a script to remove all the rows with more than 46 missing values. This works perfect on a smaller dataset. But the problem arises when I try to run it on the larger data set I get an error “cannot allocate vector size 1240 kb”. I’ve searched through previous posts and found out that it might

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I try to find the most important variables to divide my dataset as given in a categorical variable. code: Test.rf4<-randomForest(Sex~.,na.action=na.roughfix, data=Subset4, importance=TRUE, proximity=TRUE, ntree=10000, do.trace=1000, keep.forest=FALSE) My dataset contains also ordered

Hello everybody, this is my first request about R so I am sorry if I send it to a bad mail or if I am not very clear. So my problem is about the use of rfImpute from randomForest package. I am interested in imputations of missing values and I read that randomForest can make it. So i write the following code : set.seed(100); library(mlbench) library(randomForest) data(BreastCancer)

Hi there, We're trying to use randomForest to do some predictions. The test-harness for our code is pretty straightforward: library ('randomForest'); data202 <- read.csv ("random.csv", header=TRUE); x<- data202[1:50000,1:6]; y<- data202[1:50000,8]; y<- y[,drop=TRUE]; x2 <- data202[50001:60000,1:6]; y2 <- data202[50001:60000,8]; y2 <-

Dear Administrator, I am using linux (suse 10.2). While attempting rfImpute, I am getting the following error message: > Members <- rfImpute(Status ~ ., data = Members) Error in .C("classRF", x = x, xdim = as.integer(c(p, n)), y = as.integer(y), : C symbol name "classRF" not in DLL for package "randomForest". I need the help to sort out above error.

Hi I listened to all your advise and ran my data on a computer with a 64 bits procesor but i still get the same error saying "it cannot allocate a vector of that size 1240 kb" . I don't want to cut my data in smaller pieces because we are looking at interaction. So are there any other options for me to try out or should i wait for the development of more advanced computers!

Dear all, Can anybody give me some hint on the following error msg I got with using randomForest? I have two-class classification problem. The data file "sample" is: ---------------------------------------------------------- udomain.edu udomain.hcs hpclass 1 1.0000 1 not 2 NA 2 not 3 NA 0.8 not 4 NA 0.2 hp 5 NA 0.9 hp ------------------------------------------------------------ The

Hi, i try several possibilities adn looking in the archive, but didn't getting success to install j.lindsey's usefuel "library repeated" on my linux (suse9.0 with kernel 2.6.7,R.1.9.1) P.S. Windows, works fine Many thanks for help Christian chris at linux:/space/downs> R CMD INSTALL - l /usr/lib/R/library repeated WARNING: invalid package '-' WARNING:

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I tried rfImpute (randomForest) and as far as I understood should it replace NA`s using a proximity matrix: > set.seed(100000) > Subset5Imputed<-rfImpute(Sex~., data=Subset5) ntree OOB 1 2 300: 11.78% 12.36% 11.21% ntree OOB 1 2 300: 12.07% 12.64%