similar to: randomForest Tutorial

Hi, I would like to visualize a tree extracted from a random forest using getTree {randomForest}. I'm wondering if there is any way to do it directly or to convert my tree to any other class of tree repesentation, which then can be plotted? Regards, Umang Rathi ________________________________ This email message may contain proprietary, private and confidential information. The information

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

Hi R-team, While using R 2.12.2, I came across a problem that it doesn't have RPostgreSQL package in the list of "install packages". As my original code was written in R 2.11.1, I could use the RPostgreSQL package. I am moving to R 2.12.2 to use the newly added package "xtable" in newer version R 2.12.2. Please help me to solve this problem and tell me if the RPostgreSQL

I was trying to ByteCompile a package that I made. The package compiles successfully with byte compile set to FALSE. When I set ByteCompile to TRUE, I receive the following error message while doing R CMD INSTALL /usr/lib/R/bin/INSTALL: line 34: 9964 Done echo 'tools:::.install_packages()' 9965 Segmentation fault | R_DEFAULT_PACKAGES= LC_COLLATE=C "${R_HOME}/bin/R" $myArgs

Hi, I am trying to build an R package so reading the manual on CRAN. I could figure out that using imports to load functions in your namespace would be the best bet to use in the Description file. After adding to the description file, I also added it to the namespace file. I added importFrom to the namespace file with the functions required. Now when I run R CMD check on my package, I get an

H, I was trying to ByteCompile a package that I made. The package compiles successfully with byte compile set to FALSE. When I set ByteCompile to TRUE, I receive the following error message while doing R CMD INSTALL /usr/lib/R/bin/INSTALL: line 34: 9964 Done echo 'tools:::.install_packages()' 9965 Segmentation fault | R_DEFAULT_PACKAGES= LC_COLLATE=C "${R_HOME}/bin/R" $myArgs

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:

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

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

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 ).

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

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*

Dear list, I am trying to do a benchmark study for my case study. It is a regression problem. Among other models I use randomForest. Using the following code the result is around 0.628, and this make sense comparing with other methods. The Theil function implements Theil's U statistic. I do not present the definition of some variables because it is not important to understand my problem.

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

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

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

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 <-

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

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",