similar to: Pre-model Variable Reduction

Hello all, I am attempting to run an R script that makes use of RWeka. I am running SuSE Linux 9.3 with Java 1.5.0_06, R version 2.2.1, Weka 3-4-7, and Rweka 0.2-1. CLASSPATH="/usr/local/weka-3-4-7/weka.jar:/usr/local/JGR/JGR.jar" I receive the error: NewObject("weka/core/Instances","(Ljava/io/Reader;)V",...) failed Exception in thread "main"

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 R users, I request members of the R community to consider filling a short survey regarding the use of R. The survey can be found at http://goo.gl/jw1ig Please accept my apologies for posting here for a non-technical reason. The data collected will be suitably analyzed and I'll post a link to the results in the coming weeks. Thank you all for your interest and for sharing your R usage

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

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

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

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

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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

HI R users, I would appreciate information/examples/suggestions on building GUIs for R applications. I am currently working on a project that would require the following functionalities : 1) Display a window to the user. Provide a function to scan local drive and choose dataset file. 2) Display the column names for the user to choose the dependent variable and the independent variables. 3) Fit

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 UseRs, I recently started working with the RGtk2 library. The documentation is comprehensive and I've tried learning from the examples in most Gtk tutorials (which have C code). This is a little problematic, but has helped quite a bit in getting me started. I would like to create a GUI for file selection, which then displays the column names from the selected file, and provides the user

Hi useRs, I would like to know what R users are employing to get their images/plots created in R, into Excel sheets. I am aware of the various packages (xlsReadWrite, RODBC) to get data frames into R, but I would like to copy images too. Thank you for any help in solving this problem. Regards, Harsh Singhal

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 =

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,

Howdy Gurus again Thanks to Tony.Breyal, I was able to writing the following script for analyzing a text document. But I got an error with "tm' package. I don't why I got the error from the R script below. I think I followed proccess of R tm manual. I use R v2.8.1. and tm_0.3-3.zip under Win XP. Thanks in advance, Kum Hwang > # setting directory > my.path

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 useRs, I have a problem at hand which I'd think is fairly common amongst groups were R is being adopted for Analytics in place of SAS. Users would like to obtain results for logistic regression in R that they have become accustomed to in SAS. Towards this end, I was able to propose the Design package in R which contains many functions to extract the various metrics that SAS reports.

Hi All, I am trying to assemble a system that will allow me to work with large datasets (45-50 million rows, 300-400 columns) possibly amounting to 10GB + in size. I am aware that R 64 bit implementations on Linux boxes are suitable for such an exercise but I am looking for configurations that R users out there may have used in creating a high-end R system. Due to a lot of apprehensions that SAS

Hello R List, I seem to have a peculiar problem. When using time series data, I get the following error when running the acf and pacf function. Using the function acf(dtxts,plot= TRUE,xaxt = "n",col="red",na.action = na.omit) (where dtxts is a time series object created with package "xts" ) results in the error below. Error in na.omit.ts(as.ts(x)) : time series