similar to: Java related (?) problems with RWeka

-----Original Message----- From: Patrick Connolly Sent: Friday, 9 November 2012 11:29 a.m. To: Peter Alspach Subject: Interfacing R and Weka > version _ platform x86_64-unknown-linux-gnu arch x86_64 os linux-gnu system x86_64, linux-gnu status major 2 minor 15.2 year 2012 month 10 day 26 svn rev

Le jeudi 09 f?vrier 2012 ? 15:31 +0200, Kari Ruohonen a ?crit : > Hi, > I am trying to replicate a training of AttributeSelectedClassifier with > CFsSubsetEval, BestFirst and NaiveBayes that I have initially done with > Weka. Now, I am trying to use RWeka in R. > > I have a problem of passing arguments to the CfsSubsetEval, BestFirst > and NaiveBayes. I have first created an

Dear All, I am finding trouble trying to guild a Wrapper using random forest to evaluate the subsets: I do: nombi <- make_Weka_filter("weka/filters/supervised/attribute/AttributeSelection") datbin<- nombi(gene ~., data=X1X2X4X5W, control =Weka_control( S=list("weka.attributeSelection.GeneticSearch"), E=list("weka.attributeSelection.WrapperSubsetEval"),B

Hello, I have some trouble in achieving the desired parametrisation for the weka classifier functions, using the package RWeka. The problem is, that the functions result=classifier(formula, data, subset, na.action, control = Weka_control(mycontrol)) do not seem to be manipulated by the mycontrol- arguments Perhaps this should be resepected via the handlers- argument , but the

Hi everyone, I have trouble to use RWeka, I tried: (w=weather dataset, all preditors are nominal) > m<-J48(play~., data=w) > e<-evaluate_Weka_classifier(m,cost = matrix(c(0,2,1,0), + ncol = 2),numFolds = 10, complexity = TRUE,seed = 123, + class = TRUE) it gives me exactly what I want, but when I tried the same classifier on the other published data: (iris dataset has all numeric

Dear List, I´m trying to implement the functionalities from WEKA into my modeling project in R through the RWeka package. In this context I have a slightly special question about the filters implemented in WEKA. I want to convert nominal attributes with k values into k binary attributes through the NominalToBinary filter ("weka.filters.supervised.attribute.NominalToBinary"). But

Hello, I have two questions concerning the RWeka package: 1.) First question: How can one perform a cross validation, -say 10fold- for a given data set and given model ? 2.) Second question What is the correct syntax for the parametrization of e.g. Kernel classifiers interface m1 <- SMO(Species ~ ., data = iris, control =

Hi, I'm trying to use Information Gain for feature selection. There is a InfoGain implementation in Weka: *weka.attributeSelection.InfoGainAttributeEval* Is it possible to use this function with RWeka? If yes how? list_Weka_interfaces doesn't show it and there is no make function for AttributeEvaluators. Is there any other implementation of InformationGain in R? Thank you Michael

Hello, I have a trained Back Propagation Neural Network model in weka. I would like to re-evaluate the NN using R with a given input. How can I do this? I could not find an example of RWeca that applies to NN Thanks, Rui

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, I want to save a RWeka model into a file, in order to retrive it latter with a load function. See this example: library(RWeka) NB <- make_Weka_classifier("weka/classifiers/bayes/NaiveBayes") model<-NB(formula,data=data,...) # does not run but you get the idea save(model,file="model.dat") # simple save R command # ... load("model.dat") # load the model

Hi I'm trying to use RWeka to use a NaiveBayes Classifier(the Weka version). However it crashes whenever there is a NA in the class Gender Here is the.code I have with d2 as the data frame. The first call to NB doesn't make R crash but the second call does. NB <- make_Weka_classifier("weka/classifiers/bayes/NaiveBayesSimple") d2[,64]<-d2$Gender=="M"

I am out of my league with this question. The following code starts the java imaging program ImageJ from within R, and displays an image (assuming ImageJ is installed on your computer). library(RImageJ) img <- IJ$openImage( file.choose() ) #pick an available .tif file img$show() # make the image object visible # An image is now displayed # find out about the objects involved >

Dear All, I'd like to use Weka functions that are not implemented (do not have interface) in RWeka, like the Remove function and others in the future! The .java() functionality is for that purpose but I haven't seen any example with Weka functions. Could anyone give me hand in how to do it? For instace if I want to use the weka.filters.unsupervised.attribute.Remove? 1. in the R console,

Hi, I downloaded RWeka successfully. (At least I do not see, where I could have made a mistake.) Then I tried to load it by the library-command. To my surprise this did not work. Result: library(RWeka) Fehler in .jinit(c(system.file("jar", "weka.jar", package = "RWeka"), system.file("jar", : Cannot create Java Virtual Machine Fehler: .onLoad in

Hi - I have a question on RWeka. I installed the package and try to run using some examples available in the package. However, it stalls my machine for a while. I'm wondering if I need weka (which is java implementation) installed before using RWeka? Thank you. - adschai

Hi, I'm trying to use RWeka and followed the following example from the RWeka manual. ============ ## Use some example data. w <- read.arff(system.file("arff","weather.nominal.arff", package = "RWeka")) ## Identify a decision tree. m <- J48(play~., data = w) m ## Use 10 fold cross-validation. e <- evaluate_Weka_classifier(m, cost = matrix(c(0,2,1,0),

Running Weka's command line with calls to system(), like this > system("java weka.classifiers.bayes.NaiveBayes -K -t HWlrTrain.arff -o") === Confusion Matrix === a b <-- classified as 3518 597 | a = NoSpray 644 926 | b = Spray === Stratified cross-validation === === Confusion Matrix === a b <-- classified as 3512 603 | a = NoSpray

Hello: I´m trying to execute Apriori(file.arff) command of RWeka package. I´m working with: Operating System: Windows XP home R-2.4.0 RWeka_0.2-11 rJava_0.4-11 classpath= .;C:\Archivos de programa\Java\jdk1.5.0\lib;C:\Archivos de programa\R\R- 2.4.0\library\RWeka\jar An error occurs when .jnew command is executed, on class "weka/core/Instances" :

I'm a big fan of both Weka and R (quite new at R :) ), and jumped at the chance to use them together. Unfortunately, I'm running into what is probably a dumb error when trying to view info about my model. A Google search turned up 0 hits for the actual error I got (last line), but you all are smarter! My code is below, but basically my data frame (q) is imported via RODBC and has 1586