similar to: how to write randomforest in r

Dear All, For a data mining project, I am relying heavily on the RandomForest and Party packages. Due to the large size of the data set, I have often memory problems (in particular with the Party package; RandomForest seems to use less memory). I really have two questions at this point 1) Please see how I am using the Party and RandomForest packages. Any comment is welcome and useful.

Dear R-users, While making a prediction using the randomForest function (package randomForest) I'm getting the following error message: "Error in predict.randomForest(model, newdata = CV) : No forest component in the object" Here's my complete code. For reproducing this task, please find my 2 data sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).

Buenos días: Otra opción es escribir directamente el nombre de la función en la consola de R: > randomForest function (x, ...) UseMethod("randomForest") En este caso, la función randomForest() llama a UseMethod() para seleccionar el método adecuado. Podemos ver los métodos para randomForest con la función methods(): > methods(randomForest) [1] randomForest.default*

Hello I'd like to try out some functions in the package randomForest. Therefore, I did install this package. However, it is not possible to load the library, although I have R-Version 2.1.1 (i.e. later than 2.0.0). The commands I used and the Answers/Error from R is as follows: > install.packages("C://Programme//R//rw2011//library//randomForest_4.5-16.zip",

Hello, I've tried to install randomForest on a Ubuntu 8.04 Hardy Heron system. I've repeatedly rec'd the error: > install.packages("randomForest", dependencies = TRUE) ERROR: compiliation failed for package 'randomForest' ** Removing '/home/admuser/R/i486-pc-linux-gnu-library/2.6/randomForest' The downloaded packages are in

I am new to R and I'd like to use the randomForest package for my thesis (identifying important variables for more detailed analysis with other software). I have found extremely well written and helpful information on the usage of R. Unfortunately it seems to be very difficult to find similarly detailed tutorials for randomForest, and I just can't get it work with the information on

Just out of curiosity, I took the default "iris" example in the RF helpfile... but seeing the admonition against using the formula interface for large data sets, I wanted to play around a bit to see how the various options affected the output. Found something interesting I couldn't find documentation for... Just like the example... > set.seed(12) # to be sure I have

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 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 R-users, I have a problem with randomForest{outlier}. After running the following code ( that produces a silly data set and builds a model with randomForest ): ####################### library(randomForest) set.seed(0) ## build data set X <- rbind( matrix( runif(n=400,min=-1,max=1), ncol = 10 ) , rep(1,times= 10 ) ) Y <- matrix( nrow = nrow(X), ncol = 1) for( i in (1:nrow(X))){

I have search the help archives and can't find a direct reference to the following issue: When installing randomForest on under CentOS 5.2 , R version 2.7.1 with gcc 4.1.2. We receive the following error (see below, can't find –lg2c) it is in the path! root@abcsci12 ~]# R CMD INSTALL /scisys/home/yanicrk/randomForest_4.5-28.tar.gz * Installing to library

Christian -- You don't provide enough information (like a call) to answer this. I suspect, though, that you may be subsetting in a way that passes randomForest no data. I'm not aware offhand of an easy way to get this error from randomForest. I tried creating some data superficially similar to yours to see whether something would break if there were only a single value in the variable

I'm trying to do: randomForest(f, data = moths.train) But I get this error: Error in randomForest.default(m, y, ...) : Need at least two classes to do classification. When I look at the data for this, I realize there are no positive cases of this item: [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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

I have a data.frame with a series of variables tagged to a binary response ('present'/'absent'). I am trying to use randomForest to predict present/absent in a second dataset. After a lot a fiddling (using two data frames, making sure data types are the same, lots of testing with data that works such as data(iris)) I've settled on combining all my data into one data.frame

RF trains fine with X, but fails on prediction > library(randomForest) > chirps <- c(20,16.0,19.8,18.4,17.1,15.5,14.7,17.1,15.4,16.2,15,17.2,16,17,14.1) > temp <- c(88.6,71.6,93.3,84.3,80.6,75.2,69.7,82,69.4,83.3,78.6,82.6,80.6,83.5,76 .3) > X <- cbind(1,chirps) > rf <- randomForest(X, temp) > yp <- predict(rf, X) Error in predict.randomForest(rf, X) : subscript

Dear All, I would like to use a randomForest algorithm on a dataset. The set is not particularly large/difficult to handle, but it has some missing values (both factors and numerical values). According to what I found https://stat.ethz.ch/pipermail/r-help/2005-September/078880.html https://stat.ethz.ch/pipermail/r-help/2007-January/123117.html the randomForest package has a problem with missing

I received this error Error in randomForest.default(m, y, ...) : Can not handle categorical predictors with more than 32 categories. using below code library(randomForest) library(MASS) memory.limit(size=12999) x <- read.csv("D:/train_store_title_view.csv", header=TRUE) x <- na.omit(x) set.seed(131) sales.rf <- randomForest(sales ~ ., data=x, mtry=3, importance=TRUE) My

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

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 ~ .,