similar to: installing contributed programs

I have a data.frame with a series of variables tagged to a binary response ('present'/'absent'). I am trying to use randomForest to predict present/absent in a second dataset. After a lot a fiddling (using two data frames, making sure data types are the same, lots of testing with data that works such as data(iris)) I've settled on combining all my data into one data.frame

I am attempting to use randomForests to look for interesting genes in microarray data with 216genes, 2 classes and 52 samples. My data.frame is 52x217 with the last column, V217 being the class(1 or 2). When I try lung.rf <- randomForest(V217 ~ ., data=tlSA216cda, importance= TRUE, proximity = TRUE) the GUI crashes. I am running R-1.6.2 under windo$e98, and most

Hi, I am using randomForests for a classification problem. The predict function in the randomForest library, when asked to return the probabilities, has precision of two digits after the decimal. I need at least four digits of precision for the predicted probabilities. How do I achieve this? Thank you, Nagu

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

Hello all, So I am using the (formula entry) method for randomForests: randomForest(y~x1+x2+...+x39+x40,data=xxx,...) but the issue is that some of the items in that package dont take a formula entry - you have to explicitly state the y and x vector: randomForest(x=xxx[,c('x1','x2',...,'x40')],y=xxx[,'y'],...) Now my question is whether there is a function/way

For anyone who is knowledgeable about the randomForest package in R, I have a question: When I look at the variable importance for data, I see that my response variable is included along with my predictor variables. That is, I am getting a MeanDecreaseGini for my response variable, and therefore it seems as though it is being treated as a predictor variable. my code (just in case it helps) :

Hi, I'm experimenting with random forests and want to perform a binary classification task. I've tried some of the sample codes in the help files and things run, but I get a message to the effect 'you don't have very many unique values in the target - are you sure you want to do regression?' (sorry, don't know exact message but r is busy now so can't check). In

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

Hi All, My question is in regards to an error generated when using randomForest in R. Is there a special way to format the data in order to avoid this error, or am I completely confused on what the error implies? "Error in randomForest.default(m, y, ...) : Can not handle categorical predictors with more than 32 categories." This is generated from the command line: >

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

Hello everybody, I'm testing the randomForest package in order to do some simulations and I get some trouble with the prediction of new values. The random forest computation is fine but each time I try to predict values with the newly created object, I get an error message. I thought I was because NA values in the dataframe, but I cleaned them and still got the same error. What am I

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

You need to give us more details, like how you call randomForest, versions of the package and R itself, etc. Also, see if this helps you: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/32918.html Andy From: Eleni Rapsomaniki > > Dear all, > > I am trying to train a randomForest using all my control data > (12,000 cases, ~ 20 explanatory variables, 2 classes). > Because

Hi, I am trying to run randomForests on a datasets of size 500000X650 and R pops up memory allocation error. Are there any better ways to deal with large datasets in R, for example, Splus had something like bigData library. Thank you, Nagu

Dear all, I am trying to train a randomForest using all my control data (12,000 cases, ~ 20 explanatory variables, 2 classes). Because of memory constraints, I have split my data into 7 subsets and trained a randomForest for each, hoping that using combine() afterwards would solve the memory issue. Unfortunately, combine() still runs out of memory. Is there anything else I can do? (I am not using

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

Dear R-devel, Inspired by Michael Li's talk at JSM, I decided to try rpvm and snow on our two linux boxes. It only took me a couple of hours of screwing around to get it working (sooner if I had RTFM). Our setup is: 2 dual PIII-866 Xeons, one with 2GB RDRAM, the other with 1.28GB RDRAM. The first machine is acting as the NIS/NFS server. both /usr and /home are exported to the second

Greetings, I want to inquire about the memory limitations of the randomForest package. I am attempting to perform clustering analysis using RF but I keep getting the message that RF cannot allocate a vector of a given size. I am currently using the 32-bit version of R to run this analysis, are there fewer memory issues when using the 64-bit version of R? Mainly I want to be able to run RF on

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I try to find the most important variables to divide my dataset as given in a categorical variable using randomForest. Is randomForest() able to deal with count data? Or is there no difference because only the ranks are used in the trees? Thanks in advance Birgit Birgit Lemcke Institut f?r

Does anyone know a reason why, in principle, a call to randomForest cannot accept a data frame with missing predictor values? If each individual tree is built using CART, then it seems like this should be possible. (I understand that one may impute missing values using rfImpute or some other method, but I would like to avoid doing that.) If this functionality were available, then when the trees