similar to: understanding randomForest results

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 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 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 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

> From: luk > > I got the following results when I run radomForest with below > commands: > > qair <- read.table("train10.dat", header = T) > oz.rf <- randomForest(LESION ~ ., data = qair, ntree = 220, > importance = TRUE) > print(oz.rf) > > Call: > randomForest.formula(x = LESION ~ ., data = qair, ntree = > 220, 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

Full_Name: Daniel Normolle Version: 2.0.1 OS: Linux/Fedora Core 3 Submission from: (NULL) (141.214.17.5) varImpPlot in Random Forests 4.5-10 produces the error "incorrect number of subscripts on matrix" (and no plot) when applied to a randomForest object. This error did not occur with 4.5-4 or earlier versions.

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

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

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

Dear all , I would like to use the function randomForest to predict the probability of relocation failure of a GPS collar as a function of several environmental variables x (both factor and numeric: slope, vegetation, etc.) on a given area. The response variable y is thus success (0)/failure(1) of the relocation, and the sampling unit is the pixel of a raster map. My aim is to build a map

Here is a snippet to show what i'm trying to do. library(randomForest) library(ROCR) library(caret) data(iris) iris <- iris[(iris$Species != "setosa"),] fit <- randomForest(factor(Species) ~ ., data=iris, ntree=50) train.predict <- predict(fit,iris,type="prob")[,2]

Hello, I'm trying to figure out a solution online but couldn't so far. I realized that others dealt with such an error but their suggestions didn't work for me. So, I'm trying to run the randomForest() using this command: rf = randomForest(classes~., data=as.matrix(train), mtry=5, ntree=2000, importance=TRUE) and it always results in *Error: protect(): protection stack

Hi all, When running a randomForest run using the following command: forestplas=randomForest(Prev~.,data=plas,ntree=200000) print(forestplas) I get the following result: Call: randomForest(formula = Prev ~ ., data = plas, ntree = 2e+05, importance = TRUE) Type of random forest: regression Number of trees: 2e+05 No. of variables tried at each split: 5

Hello ALL, I'm unsure how to interpret partial dependence plots and had 2 questions: Does the randomForest library scale partial dependence plots such that zero on the y axis is mean(y)? I am also unclear as to why the range of y on these plots is smaller than what is in the dataset. Also I have noticed that partial dependence plots cluster around the mean response. That is, if I'm

I've been using the R package randomForest but there is an aspect I cannot work out the meaning of. After calling the randomForest function, the returned object contains an element called prediction, which is the prediction obtained using all the trees (at least that's my understanding). I've checked that this prediction set has the error rate as reported by err.rate. However, if I

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 ).

Hi, I have a randomforest object "cost.rf" that was created in splus 8.0, now I need to use this trained RF model in R. So in Splus, I dump the RF file as below data.dump("cost.rf", file="cost.rf.txt", oldStyle=T) then in R, restore the dumped file, library(foreign) data.restore("cost.rf.txt") it works fine and able to restore the

I've run the function randomForest with importance=T. All my variables (predictors and the dependent variable) are numeric. rf<-randomForest(formula, data=mydata, importance=T, etc.) my results object "rf" contains predictor importances: rf$importance I am seeing two columns: %IncMSE IncNodePurity V1 -0.01683558 58.10910 V2 0.04000299 71.27579 V3 0.01974636