similar to: na.action in randomForest

A few days ago I asked whether there were options other than na.action=na.fail for the R port of Breiman?s randomForest; the function?s help page did not say anything about other options. I have since discovered that a pdf document called ?The randomForest Package? and made available by Andy Liaw (who made the tool available in R---thank you) does discuss an option. It is an implementation of

In trying to execute this line in R (Version 1.7.1 (2003-06-16), under windows XP pro), with the randomForest library (about two weeks old) loaded, the program crashed: bost4rf <- rfImpute(TargetDensity~.,data=bost4rf0) Specifically, an XP dialog box popped up, saying ?R for windows GUI front-end has encountered a problem and needs to close.? That was the dialog saying asking whether I

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

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

Dear R-users, I have a problem with randomForest{outlier}. After running the following code ( that produces a silly data set and builds a model with randomForest ): ####################### library(randomForest) set.seed(0) ## build data set X <- rbind( matrix( runif(n=400,min=-1,max=1), ncol = 10 ) , rep(1,times= 10 ) ) Y <- matrix( nrow = nrow(X), ncol = 1) for( i in (1:nrow(X))){

Greetings. I'm trying to determine whether to use rpart or randomForest for a classification tree. Has anybody tested efficacy formally? I've run both and the confusion matrix for rf beats rpart. I've looking at the rf help page and am unable to figure out how to extract the tree. But more than that I'm looking for a more comprehensive user's guide for randomForest including

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

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

After entering ?library(rpart)?, I tried to plot an existing rpart tree, and got this error message: Error: couldn't find function "plot.rpart". However, ??plot.rpart? does bring up the help for the function. The same things occur for text.rpart, although print(my.tree) does work. So, I tried to re-install rpart using Packages | Install from CRAN, but then I get this

I'm having a weird problem with length(), in R1.6.1 under windows2000. I have a dataframe called byyr, with ten columns, the first of which is named cnd95. summary(byyr) shows that byyr$cnd95 contains the factor level "tr" 66 times. Also, when I enter byyr$cnd95 at the command line, I can count 66 "tr" elements in the resulting vector. However, when I enter n95trt <-

I'm having trouble calling randomForest::partialPlot programmatically. It tries to use name of the (R) variable as the data column name. Example: library(randomForest) iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE, proximity=TRUE) partialPlot(iris.rf, iris, Sepal.Width) # works partialPlot(iris.rf, iris, "Sepal.Width") # works (function(var.name)

Hi all, I am trying to play around with the randomForest function for classification. I know its performance is great. I am currently using the default options. It has many options. How do I further tweak the options so that I can make its performance even better? What are the options that are mostly used? Thanks a lot! M [[alternative HTML version deleted]]

Dear R-help, This is about randomForest's handling of NA and NaNs in test set data. Currently, if the test set data contains an NA or NaN then predict.randomForest will skip that row in the output. I would like to change that behavior to outputting an NA. Can this be done with flags to randomForest? If not can some sort of wrapper be built to put the NAs back in? thanks, Clayton

I can't get identify to work, using R 2.0.1 under windows xp pro, service pack 2. Here's what I enter, and the result: > plot((our.frame2$c1),(our.frame2$c9)) # Produces desired plot > identify(our.frame2$c1) # Plot comes to forefront, so I select a point warning: no point with 0.25 inches numeric(0) Is my call to identify correct? The help page for indentify (from

I'm using R in a statistics class, and when I project the console, the font is smaller than ideal. I've checked the faq's, the manual, and the help system as best I can, and I don't see how to change the font size. Can it be changed from within a session, or will I have to ask the folks who installed the program on the server I use in classes to set it (assuming that can be

Hi there, I am interested in using R for machine learning (supervised classification). Currently, I have been investigating especially the rpart, tree, and randomForest package, and have achieved first results. are there any experiences, how the learned classificators could be used in e.g. C ? in other words, I want to "transfer" the learned predictor from R to C-code. for e.g. rpart,

I've looked every way I can think of for help on trellis.datasets, but nothing comes up for me. Please help me find information on what is included, and how to get at those data. Thanks. Dave Parkhurst

Hello, I am attempting to train a random forest model using the randomForest package on 500,000 rows and 8 columns (7 predictors, 1 response). The data set is the first block of data from the UCI Machine Learning Repo dataset "Record Linkage Comparison Patterns" with the slight modification that I dropped two columns with lots of NA's and I used knn imputation to fill in other gaps.

Should one expect minor numerical differences between 64bit and 32bit R on Windows? Hunting around the lists I've not been able to find a definitive answer yet. Seems plausible using different precision arithmetic, but waned to confirm from those who might know for sure. BACKGROUND A colleague was trying to replicate some modelling results (from a soon to be published book) using rpart, ada,

Hello, I came across a problem when building a randomForest model. Maybe someone can help me. I have a training- and a testdataset with a discrete response and ten predictors (numeric and factor variables). The two datasets are similar in terms of number of predictor, name of variables and datatype of variables (factor, numeric) except that only one predictor has got 20 levels in the training