similar to: Random Forests 4.5-10 varImpPlot (PR#7844)

Hi everyone, I have another "Random Forest" package question: - my (presumably incorrect) understanding of the varImpPlot is that it should plot the "% increase in MSE" and "IncNodePurity" exactly as can be found from the "importance" section of the model results. - However, the plot does not, in fact, match the "importance"

Dear R-devel, Kurt had alerted me to the problem that the randomForest package that I maintain has been failing checks in R-devel. However, I just can't see why or where it's failing. I'd very much appreciate any pointer. The failure occur when running the example code in varImpPlot.Rd: > varImpPlot(mtcars.rf) Error in par(opar) : invalid value specified for graphical parameter

Hi, I'm using package "caret" to rank predictors using random forest model and draw predictors importance plot. I used below commands: rf.fit<-randomForest(x,y,ntree=500,importance=TRUE) ## "x" is matrix whose columns are predictors, "y" is a binary resonse vector ## Then I got the ranked predictors by ranking

Hello, I am using the R randomForest package to classify variable stars. I have a training set of 1755 stars described by (too) many variables. Some of these variables are highly correlated. I believe that I understand how randomForest works and how the variable importance are evaluated (through variable permutations). Here are my questions. 1) variable importance error? Is there any ways

Hi I am having a problem using varImpPlot in randomForest. I get the error message "Error in plot.window(xlim = xlim, ylim = ylim, log = "") : need finite 'xlim' values" When print $importance, several variables have NaN under %IncMSE. There are no NaNs in the original data. Can someone help me figure out what is happening here? Thanks! [[alternative HTML

So I've been working with Random Forests ( R library is randomForest) and I curious if Random Forests could be applied to classifying on a real time basis. For instance lets say I've scored fraud from a group of transactions. If I want to score any new incoming transactions for fraud could Random Forests be used in that context. Linear Regression is nice in that it is very easy to

Dear all, I would like to use a tree regression method to analyze my dataset. I am interested in the fact that random forests creates in-bag and out-of-bag datasets, but I also need an estimate of support for each split. That seems hard to do in random forests since each tree is grown using a subset of the predictor variables. I was thinking of setting mtry = number of predictor variables,

Dear Random Forests gurus, I have a question about R^2 provided by randomForest (for regression). I don't succeed in finding this information. In the help file for randomForest under "Value" it says: rsq: (regression only) - "pseudo R-squared'': 1 - mse / Var(y). Could someone please explain in somewhat more detail how exactly R^2 is calculated? Is "mse"

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

Hello, I was wondering if anyone knew of an R function/R code to plot bivariate (3 dimensional) partial dependence plots in random forests (randomForest package). It is apparently possible using the rgl package (http://esapubs.org/archive/ecol/E088/173/appendix-C.htm) or there may be a more direct function such as the pairplot() in MART (multiple additive regression trees)? Many

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 <-

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 all, I have a dataset where each point is assigned to a class A, B, C, or D. Each point is also assigned to a study site. Each study site is coded with a number ranging between 1-100. This information is stored in the vector studySites. I want to run randomForests using stratified sampling, so I chose the option strata = factor(studySites) But I am not sure how to control the number of

I'm calculating nodes using Random Forest in R but i only get nodes for a fraction of the compounds i want to calculate, the rest is ommited and is not printed in the output file, (i'm working with 3012 compounds). What can i do to have nodes printed for all the compounds. Thanks Gon?alo

Hi, I have done an analysis using 'rpart' to construct a Classification Tree. I am wanting to retain the output in tree form so that it is easily interpretable. However, I am wanting to compare the 'accuracy' of the tree to a Random Forest to estimate how much predictive ability is lost by using one simple tree. My understanding is that the error automatically displayed by the two

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:

Hi, I had two questions regarding random forests for regression. 1) I have read the original paper by Breiman as well as a paper dicussing an application of random forests and it appears that the one of the nice features of this technique is good predictive ability. However I have some data with which I have generated a linear model using lm(). I can get an RMS error of 0.43 and an R^2 of

How do I read/interpret the output of varImpPlot() for a randomForest object? Regards, Divya -------------------------------------------------------- [[alternative HTML version deleted]]

Hello R Gurus, I am perplexed by the different results I obtained when I ran code like this: set.seed(100) test1<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200) predict(test1, newdata=cbind(NewBinaryY, NewXs), type="response") and this code: set.seed(100) test2<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200, xtest=NewXs, ytest=NewBinarY) The