similar to: relevance vector machines for classification

Dear R users, Is documentation for the netlabR package available in English? If not does anyone know if or when it will be? Regards, Keith ****************************************************************************This e-mail and any files transmitted with it are confidential and are intended solely for the use of the individual or entity to whom it is addressed. If you are not

When plotting a lars object, I cannot find a way to plot solid lines. Even when the arguments breaks=F and lty="solid" are used, the vertical lines at the break points do not plot but asterisks indicating the breaks still plot as part of each path leaving solid lines broken up by asterisks at the break points. I'm using the following code. larsfit <-

I want to be able to load a library in a specified position using the pos= argument and have any subsequent library required by the one I'm loading go into a specified library as well. For example, in loading caret, it requires and loads lattice as well. When I specify that caret goes into position 9, lattice goes into position 2 by default. Is there a way to specify that by loading a

--- El mié, 31/12/08, Gabriel Ibarra <zackvsc@yahoo.es> escribió: De: Gabriel Ibarra <zackvsc@yahoo.es> Asunto: RVM help Para: r-help@r-project.org Fecha: miércoles, 31 diciembre, 2008 3:03 Hello:   I would need some help on the use of RVM in the kernlab package. Questions: 1. Is there an optimal ratio between available number of cases and number of vectors? 2. Does something

I have a longitudinal data set in long format and I want to run individual regressions. I do this by using the by() function as follows: temp <- by(tolerance.pp, tolerance.pp$id, function(x) summary(lm(tolerance ~ time, data=x))) This works fine. Coefficients for the first two subjects are shown below. Coefficients: Estimate Std. Error t value Pr(>|t|)

Hi there, I'm trying to do a regression experiment on a multidimensional dataset where both x and y in the model are multidimensional vectors. I'm using R version 2.9.2, updated packages, on a Linux box. I've tried gausspr(), ksvm() and rvm(), and the models are computed fine, but I'm always getting the same error message when I try to use predict(): "Error in

I have a data set called datastep4 with 211484 rows and 95 columns > dim(datastep4) [1] 211484 95 The first few column names are given below, note the first one is "RESPONDED" > names(datastep4)[1:5] [1] "RESPONDED" "VAR_30" "VAR_31" "VAR_32" "VAR_33" A table of RESPONDED shows mostly zeros >

Hi, according to ?rvm the relevance vector machine function as implemented in the kernlab-package has an argument 'cross' with which you can perform k-fold cross validation. However, when I try to add a 10-fold cross validation I get the following error message: Error in match.arg(type, c("C-svc", "nu-svc", "kbb-svc", "spoc-svc",

Hi, I am trying to perform relevance vector machines with the rvm-function from kernlab. On one dataset I get this message: Setting default kernel parameters Error in if (length(data) != vl) { : RMate stopped at line 0 of selection missing value where TRUE/FALSE needed Calls: rvm ... .local -> backsolve -> as.matrix -> chol -> diag -> array can someone explain this error

A new version of kernlab has just been released. kernlab is a kernel-based Machine Learning package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr()

A new version of kernlab has just been released. kernlab is a kernel-based Machine Learning package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr()

Hello list, I am faced with a two-class classification problem with highly asymetric class sizes (class one: 99%, class two: 1%). I'd like to obtain a class probability model, also introducing available information on the class prior. Calling kernlab/ksvm with the line > ksvm_model1<-ksvm(as.matrix(slides), as.factor(Class), class.weights= c("0" =99, "1" =1),

Hello, In my ongoing quest to develop a "best" model, I'm testing various forms of SVM to see which is best for my application. I have been using the SVM from the e1071 library without problem for several weeks. Now, I'm interested in RVM and LSSVM to see if I get better performance. When running RVM or LSSVM on the exact same data as the SVM{e1071}, I get an error that I

Hi everybody, I''m using R on a 64-bit Ubuntu 9.04 (Jaunty). I prefer to install R packages from source, even if they are available in Synaptic. The problem is that I can''t install/compile kernlab. Everything works fine until it gets to the lazy loading part: ** preparing package for lazy loading Creating a new generic function for "terms" in "kernlab"

BTW, have you look at MMTk (http://jikesrvm.org/MMTk) ? This is the garbage collection library that underlies JikesRVM. It is a 'research-oriented' implementation, meaning that it has lots of configurable settings and plugin interfaces for implementing a broad range of collection algorithms. I was amused by the fact that "building a hybrid copying/mark-sweep collector" is

Hi, For another trainingset I get this error message, which again is rather cryptic to me: Setting default kernel parameters Error in array(0, c(n, p)) : 'dim' specifies too large an array RMate stopped at line 0 of selection Calls: rvm ... .local -> backsolve -> as.matrix -> chol -> diag -> array thanks for any suggestions!

Dear R-community, I was using SVM regression (svm {e1071}) for predictions of single soil properties of a huge data set (3000 samples). There are for the eps-regression using the radial basis kernel three optimization parameters needed. To make things easier (using only two optimization parameters and not loosing performance) I wanted to use LS SVM regression (lssvm{kernlab}). But it

Hello, I would like to know, whether for the support vector classification function ksvm() the response values stored in object at ymatrix are cross validated outputs/predictions: Example code from package kernlab, function ksvm: library(kernlab) ## train a support vector machine filter <- ksvm(type~.,data=spam,kernel="rbfdot",kpar=list(sigma=0.05),C=5,cross=3) filter filter at

Dear R users, I have recently encountered a problem with using the function `library` in order to load the package `kernlab`. My output of sessionInfo() is as follows: R version 2.10.1 (2009-12-14) x86_64-unknown-linux-gnu locale: [1] C attached base packages: [1] stats graphics grDevices utils datasets methods base I have installed the package by

Hi everyone, I am trying to install kernlab package, but failed many times by now on CentOS 6 operating system. FYI, I have no problem with this package installation on windows platform. Here is the error message: trying URL 'http://cran.wustl.edu/src/contrib/kernlab_0.9-18.tar.gz' Content type 'application/x-gzip' length 1069148 bytes (1.0 Mb) opened URL