similar to: Random Forest Bug

Hi all, Can some one tell me the difference between the following two formulas? 1. epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) 2.epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) [[alternative HTML version deleted]]

Dear R-helpers, I'm working on mass spectra in randomForest/R, and following the recommendations for the case of noisy variables, I don't want to use the default mtry (sqrt of nvariables), but I'm not sure up to which proportion mtry/nvariables it makes sense to increase mtry without "overtuning" RF. Let me tell my example: I have 106 spectra belonging to 4 classes, the

Hello, Does anybody know if the outscale option of randomForest yields the standarized version of the outlier measure for each case? or the results are only the raw values. Also I have notice that this measure presents very high variability. I mean if I repeat the experiment I am getting very different values for this measure and it is hard to flag the outliers. This does not happen with two other

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

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))){

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

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%

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

Apologies for any obtuseness in the following. We have been working on Version 2.0 of the randomSurvivalForest CRAN package and we're encountering a perplexing 'memory not mapped' segfault that we believe is "influenced" by GC. We essentially have two R functions, rsf.default(..), and predict.rsf(..) and two corresponding entry points, rsfGrow(...), and rsfPredict(...),

Good Day, I'm using the randomForest package to perform a classification. If I supply weights to the optional classwt argument are proximity values computed as a weighted average? I understand that the forest will possibly change as a function of the particular weights I supply. Thanks in advance. Mike Michael Fugate Los Alamos National Laboratory Mail Stop MS-F600, Los Alamos, NM

Hi, I need help with the randomForest prediction. i run the folowing code: > iris.rf <- randomForest(Species ~ ., data=iris, > importance=TRUE,keep.forest=TRUE, proximity=TRUE) > pr<-predict(iris.rf,iris,predict.all=T) > iris.rf$votes[53,] setosa versicolor virginica 0.0000000 0.8074866 0.1925134 > table(pr$individual[53,])/500 versicolor virginica 0.928

Dear R-experts, Here below a toy example with some error messages, especially at the end of the code (Tuning the parameters). Your help to correct my R code would be highly appreciated. ####################################### #libraries library(lattice) library(ggplot2) library(caret) library(randomForest) ?? #Data

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

Dear R users, is there any possibility to get an object-name completion within the R command line (UNIX-version of R). As I got to know from the FAQ that this is possible from within Emacs (ESS), but without using Emacs? Thank's Ingo -- ----------------------------------------------------------------- Ingo Roeder Institute for Medical Informatics, Statistics and Epidemiology

Hi, everyone, I am a newbie on R. Now I want to do image pixel classification by random forest. But I has not a clear understanding on random forest. Here is some question: As for an image, for example its size is 512x512 and has only one variable -- gray level. The histogram of the image looks like mixture Gaussian Model, say Gauss distribution (u1,sigma1), (u2,sigma2),(u3,sigma3). And a

Hi, I am currently writing code to input a few thousand files, run them through the Random Forests package, and then output corresponding results. When I use the code below: zz<-textConnection("ex.lm.out", "w") sink(zz)

Hi I am new to R so I apologize if this is trivial. I am trying to predict the resistance or susceptibility of my sequences to a certain drug with a randomForest function from a file with amino acids on each of the positions in the protein. I ran the following: > library(randomForest) > > path <- "C:\\..." > path2 <- "..." > name <-

On Thu, Mar 20, 2014 at 3:29 PM, Peter Collingbourne <peter at pcc.me.uk> wrote: > > On Mon, Mar 17, 2014 at 10:28:52AM -0700, Tom Roeder wrote: > > On Tue, Feb 25, 2014 at 5:41 PM, Eric Christopher <echristo at gmail.com>wrote: > > > > > On Mon, Feb 24, 2014 at 4:33 PM, Tom Roeder <tmroeder at google.com> wrote: > > > > > > > >