similar to: predict.randomForest

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

Hi, erveryone, I show much thanks to Andy and Matthew on former questions. I now sample only a small segment of a image can segment the image into several classes by RandomForest successfully. Now I have some confusion on it: 1. What is the internal component classifier in RandomForest? Are they the CART implemented in the rpart package? 2. I use training samples to predict new samples. But

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

Greetings R Users! I am posting to inquire about the proximity matrix in the randomForest R-package. I am having difficulty pushing very large data through the algorithm and it appears to hang on the building of the prox matrix. I have read on Dr. Breiman's website that in the original code a choice can be made between using an N x N matrix OR to increase the ability to compute large

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

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

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) :

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

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 ).

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

RF trains fine with X, but fails on prediction > library(randomForest) > chirps <- c(20,16.0,19.8,18.4,17.1,15.5,14.7,17.1,15.4,16.2,15,17.2,16,17,14.1) > temp <- c(88.6,71.6,93.3,84.3,80.6,75.2,69.7,82,69.4,83.3,78.6,82.6,80.6,83.5,76 .3) > X <- cbind(1,chirps) > rf <- randomForest(X, temp) > yp <- predict(rf, X) Error in predict.randomForest(rf, X) : subscript

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

I would like to ask a general question about the randomForest predict function and how it handles No Data values. I understand that you can omit No Data values while developing the randomForest object, but how does it handle No Data in the prediction phase? I would like the output to be NA if any (not just all) of the input data have an NA value. It is not clear to me if this is the default or

Hi, I wonder if anyone knows a study dealing with the minimum valid number of observations when using CART?. On top of that, when using RandomForest, is it possible to obtained a interpretable tree model as the graphical output of the analysis, just like in "rpart"? Thanks a lot in advance Javier Lozano Universidad de Le?n Spain

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

Our CEO, Dr. Dan Steinberg, is planning to visit Europe in May. He would like the opportunity to introduce statisticians (and statistically minded people) to data mining, data mining applications and to forefront data mining tools. Our algorithms are probably familiar to many statisticians (CART, MARS, MART, TreeNet and RandomForests), although it isn't necessary to be a statistician to

I have a set of timestamp data that I have in a text file that I would like to import into R for analysis. The timestamps are formated as follows: DT_1,DT_2 [2006/08/10 21:12:14 ],[2006/08/10 21:54:00 ] [2006/08/10 20:42:00 ],[2006/08/10 22:48:00 ] [2006/08/10 20:58:00 ],[2006/08/10 21:39:00 ] [2006/08/04 12:15:24 ],[2006/08/04 12:20:00 ] [2006/08/04 12:02:00 ],[2006/08/04 14:20:00 ] I can get

Dear R-help, I'm sure this is contained within the documentation, but I have not yet located it (with good effort nonetheless). I am attempting to install the binary for randomForests. After unpacking the zip, I extracted the contents to my R-2.8.1 folder. However, when I try to execute the command library(randomForest) I get an unable to locate error. Is there another library request that

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 new to R, randomForest and I have read about how to use it in your old mails. I have also run the predictions examples from CRAN. But I still don't understand how to use it right. The thing that I don't understand is how to run the result from the randomForest on one line (post) with newdata to get a good guess. What I mean is if I put in a new observation of iris how do I