[R] rug() colors

Hello, I have a simple question on rug(). Currently there is only one color possible for the rug. Is it possible to plot a the rug with different colors, for each rug item ? Thx. Bjoern __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mail

[R] library kernlab, ksvm(x,y,scaled=T)

Hello, I encountered the following problem with the parameter scaled in ksvm() from package kernlab: [Package kernlab version 0.9-5] library(kernlab) > svp =ksvm(x=mydata,y=y,scaled=T) Using automatic sigma estimation (sigest) for RBF or laplace kernel > svp

[R] kernlab ksvm() cross-validation prediction response vector

Hello, I would like to know, whether for the support vector classification function ksvm() the response values stored in [EMAIL PROTECTED] are cross validated outputs/predictions: Example code from package kernlab, function ksvm: library(kernlab) ## train a support vector machine filter <- ksv

[R] RWeka cross-validation and Weka_control Parametrization

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

[R] text() and vector arguments like adj

Hello, I remarked that the function ## Default S3 method: text (x, y = NULL, labels = seq(along = x), adj = NULL,pos = NULL, offset = 0.5, vfont = NULL,cex = 1, col = NULL, font = NULL, ...) accepts vectors of arguments (of the same length) except for the parameter adj. When passing a vector of

Re: [R] RWeka control parameters classifiers interface

Hi, many thanks for the answer. It ist true, that for example m1 <- SMO(Species ~ ., data = iris, control = Weka_control( K = "weka.classifiers.functions.supportVector.PolyKernel")) m2 <- SMO(Species ~ ., data = iris, control = Weka_control( K = "weka.classifiers.functions.supportVector

[R] RWeka control parameters classifiers interface

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

[R] arules and frequent pattern tree

Hello, package "arules" is very well suited for mining association rules, interfacing implementations of "apriori" and "eclat". What is not realised is an implementation of the Frequent Pattern Tree (Han, J., Pei, J. and Yiwen, Y. (2000). There are several free implementations avail

Re: [R] Surprise when mapping matrix to image

Hello, I wonder if image(t(x)[ncol(x):1, ]) can do the job correct! perhaps this does the job better: image(t(x)[,nrow(x):1]) Björn From: Prof Brian Ripley Date: Fri 27 Aug 2004 - 06:43:50 EST On Thu, 26 Aug 2004, Glynn, Earl wrote: > Start with: > > > x <- c(1:7,1) > > dim(x) <- c(2,4)