similar to: Help predicting random forest-like data

Hello, I am struggling to produce an MDS plot using the randomForest package with a moderately large data set. My data set has one categorical response variables, 7 predictor variables and just under 19000 observations. That means my proximity matrix is approximately 133000 by 133000 which is quite large. To train a random forest on this large a dataset I have to use my institutions high

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

I am using R-2.6.0 on FreeBSD 8.0-CURRENT (i386). In the last days I had problems when building packages SparseM, lme4 and randomForest. The below message shows for randomForest, that 'libgfortran' was not found. The same error appeared with SparseM and lme4. --------------------------------- R CMD INSTALL randomForest_4.5-19.tar.gz * Installing to library

Hello, I have openSUSE 11.1 Trying to install randomForest as SU after invoking R install.packages("randomForest") and I get this * Installing *source* package ‘randomForest’ ... ** libs gcc -std=gnu99 -I/usr/lib/R/include -I/usr/local/include -fpic -O2 -c classTree.c -o classTree.o gcc -std=gnu99 -I/usr/lib/R/include -I/usr/local/include -fpic -O2 -c regTree.c -o

Hi all, When I tried to install package randomForest, it gave out the following error message: " > install.packages("randomForest", dependencies = TRUE) trying URL 'http://www.lmbe.seu.edu.cn/CRAN/src/contrib/randomForest_4.5-12.tar.gz' Content type 'application/x-gzip' length 82217 bytes opened URL ================================================== downloaded

Hello, I am running randomForest models on a number of species. I would like to be able to automate the printing of dependence plots for the most important variables in each model, but I am unable to figure out how to enter the variable names into my code. I had originally thought to extract them from the $importance matrix after sorting by metric (e.g. %IncMSE), but the importance matrix is n

I installed RF on Linux OpenSuSe 11.1 and while it did install and did run a model I had created on Windows correctly, it gave me a lot of "uninitialized" warnings. I don't know if these are significant and so am a little concerned even though my model ran. Any thoughts? Thanks R version 2.10.0 (2009-10-26) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN

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

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 am having some trouble using R 2.13.1 for generating a pmml object of of class "c('randomForest.formula', 'randomForest')" I see that these methods are available: > methods(pmml) [1] pmml.coxph* pmml.hclust* pmml.itemsets* pmml.kmeans* pmml.ksvm* pmml.lm* pmml.multinom* pmml.nnet* pmml.rpart* [10] pmml.rsf* pmml.rules* pmml.survreg*

Hi, Can someone please offer me some guidance? I imported some data. One of the columns called "JOBTITLE" when imported was imported as a factor column with 416 levels. I subset the data in such a way that only 4 levels have data in "JOBTITLE" and tried running randomForest but it complained about "JOBTITLE" having more than 32 categories. I know that is the limit

Dear All, I am using randomForest to predict the final selling price of some items. As it often happens, I have a lot of (noisy) historical data, but the question is not so much about data cleaning. The dataset for which I need to carry out some predictions are fairly recent sales or even some sales that will took place in the near future. As a consequence, historical data should be somehow

Hello! I want to estimate strength and correlation of RandomForest, but in package "randomForest" there is not an interface to get it. I think I must to change the source code. Is there any advise? Thanks, Li [[alternative HTML version deleted]]

I am trying to run a partialPlot with Random Forest (as I have done many times before). First I run my forest... Cell is a 6 level factor that is the dependent variable - all other variables are predictors, most of these are factors as well. predCell<-randomForest(x=tempdata[-match("Cell",names(tempdata))],y=tempdata$Cell,importance=T) Then I try my partial plot to look at the

Hi All, I am looking for a reference that explains how the randomForest function in the randomForest package uses the classwt parameter. Here: http://tolstoy.newcastle.edu.au/R/e4/help/08/05/12088.html Andy Liaw suggests not using classwt. And according to: http://r.789695.n4.nabble.com/R-help-with-RandomForest-classwt-option-td817149.html it has "not been implemented" as of 2007.

After checking the original data in Excel for blanks and running Summary(cm3) to identify any null values in my data, I'm unable to identify an instances. Yet when I attempted to use the data in Random Forest, I get the following error. Is there something that Random Forest is reading as null which is not actually null? Is there a better way to check for this? > library(randomForest) >

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, here are three results of random forest (version 4.0-1). The results seem to be more or less the same which is strange because I changed the classwt. I hoped that for example classwt=c(0.45,0.1,0.45) would result in fewer cases classified as class 2. Did I understand something wrong? Christian x1rf <- randomForest(x=as.data.frame(mfilters[cvtrain,]),

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 users, I have a question on the confusion matrix generated by function randomForest. I used the entire data set to generate the forest, for example: > print(iris.rf) Call: randomForest(formula = Species ~ ., data = iris, importance = TRUE, keep.forest = TRUE) confusion setosa versicolor virginica class.error setosa 50 0 0 0.00