similar to: randomForest get tree

Hello all, I am currently working with rpart to classify vegetation types by spectral characteristics, and am comming up with poor classifications based on the fact that I have some vegetation types that have only 15 observations, while others have over 100. I have attempted to supply prior weights to the dataset, though this does not improve the classification greatly. Could anyone supply some

Friends, I have noticed that many publications that use RF report variable importance as a function of mean decrease in accuracy rather than mean decrease in gini. Am I correct that the mean decrease in accuracy is just the mean decrease in gini divided by 100? Thanks, Helen Mills Poulos Yale School of Forestry

I'm working with the randomForest package and have successfully build a model. I'd like to go one step further however, and use the output from the model to construct a map using the output. My input data are spatial, and I have an independent set of rasterize maps for each of the predictor variables, to map the classification solution. Using the getTree function: >

What does a status value of -3 mean when I do a regression with RF and use the getTree function? left daughter right daughter split var split point status prediction 1 2 3 11 4.721000e+03 -3 15.8489576 2 4 5 5 6.500000e+00 -3 11.3240895 3 6 7 10 6.790674e+02 -3 23.7250079 4

The vegan package has non-metric multidimensional scaling in addition to a variety of other ordination techniques. Regards, Helen Mills Poulos Yale University

Hello, I have a question concerning decision trees coming from RWeka : library(RWeka) m =J48(Species~.,data=iris) How could such a decision tree be transferred into a matrix, pretty much in the same fashion, as it is done by getTree() in library(ofw) library(ofw) data(srbct) attach(srbct) ##ofwCART learn.cart.keep <- ofw(srbct,

Hi All, In the script below, the importance measure for column 4 (ie MeanDecreaseGini) indicated "Inf" for V7. Running the getTree command showed that "V7" had been selected at least twice in one of the trees for Random Forest. So the "Inf" command was not generated as a result of dividing the sum of the decreases by 0. Any suggestions on what may be causing the

[sending this to R-devel, since it may be of general interest and could possibly lead to better instructions in the "R-exts" manual] Is this another case where only .Call() or .External() [or a fudge factor] solve my problem? Following situation: I use dyn.loaded C code to compute a recursive tree structure from my data. Beforehand I don't know how big the tree will

Hi, I would like to visualize a tree extracted from a random forest using getTree {randomForest}. I'm wondering if there is any way to do it directly or to convert my tree to any other class of tree repesentation, which then can be plotted? Regards, Umang Rathi ________________________________ This email message may contain proprietary, private and confidential information. The information

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

I am having trouble calling the lmer function in the {Matrix} package. I first installed and loaded {Matrix} as follows: > install.packages("Matrix") > library(Matrix) The package loaded successfully, however when I attempted to call lmer, I received the following message: Error: could not find function "lmer" I also tried: < ?lmer which produced no search

Dear all, I am working with randomForest package and I am interested in examining the "Gini importance" measures that are used as a general indicator of feature relevance. Is there a possibility of getting the Gini measure that is being estimated in each tree by the output of the getTree() function? Thanks a lot, Chrysanthi [[alternative HTML version deleted]]

Hi there, I have been using the example provided bellow for a while, and It was working without any problem. Nevertheless, just since 2-3 days is not working, probably because I did update.packages(). I have tried to re-install the older versions of the packages Hmisc() and xtable(), but still does not work. Can you run this example, and tell me if you got the same problems? I use

Hi, I'm using the package randomForest to generate a classifier for the exemplary iris data set: data(iris) iris.rf<-randomForest(Species~.,iris) Is it possible to print all decision trees in the generated forest? If so, can the trees be also written to disk? What I actually need is to translate the decision trees in a random forest into equivalent C++ if-then-else constructs to

Using Tinn-R (in windows) is possible to run latex and R from the same editor, which was great. Now, I am using Ubuntu-linux, which has been better than WinXP. Currently, I am using Emacs (and then install ESS) for running LaTeX and R from a same editor-program (like i was doing with Tinn-R in windows). Nevertheless, and even though i have been using Emacs for almost 2 years, it is not as

Dear All: I am using lmer() {lme4} to analyze results from a reciprocal transplant experiment where the response variable is modeled as a function of two fixed effects and their interaction. Example data follow: #library(lme4) #library(gmodels)

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. 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

I have created a script that gets a snapshot and backs the snapshot to tape. I would like to keep the snapshot around for one week, than destroy it using the following command: "zfs destroy snapshotlvolume." Is there a way to automate the destroying of snapshots that are older that x amount of days? This message and its attachments may contain legally privileged or