search for: kernlabs

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, I tried to load the package "kernlab" under R-v11 and R-v10, however it gave error message: Error in library.dynam(lib, package, package.lib) : shared library 'kernlab' not found In addition: Warning message: package 'kernlab' was built under R version 2.12.0 Error: package/namespace load failed for 'kernlab' Has anybody loaded this successfully before?

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

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"

Hello, I am using kernlab to do some binary classification on aminoacid strings. I am using a custom kernel, so i use the kernel="matrix" option of the ksvm method. My (normalized) kernel matrix is of size 1309*1309, my results vector has the same length. I am using C-svc. My kernlab call is something similiar to this: ksvm(kernel="matrix", kernelMatrix, trainingDataYs,

hi, I am using R's "kernlab" package, exactly i am doing classification using ksvm(.) and predict.ksvm(.).I want use of custom kernel. I am getting some error. # Following R code works (with promotergene dataset): library("kernlab") s <- function(x, y) { sum((x*y)^1.25) } class(s) <- "kernel" data("promotergene") gene <- ksvm(Class ~ .,

Hi, this is a question about the R package kernlab. I use kernlab as a library in a C++ program. The host application defines a graph kernel (defined by me), generates a gram matrix and trains kernlab directly on this gram matrix, like this: regm<-ksvm(K,y,kernel="matrix"), where K is the n x n gram kernelMatrix of my kernel, and y is the R-vector of quantitative target values.

Dear all, I was wondering if someone can help me. I am learning SVM for classification in my research with kernlab package. I want to know about classification performance using Area Under Curve (AUC). I know ROCR package can do this job but I found all example in ROCR package have include prediction, for example, ROCR.hiv {ROCR}. My problem is how to produce prediction in SVM and to find

Hello, After upgrading to 2.7.2 this morning via the cran repository, I get the following error when calling R via the command line: Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared library '/home/jstumpf/R/i486-pc-linux-gnu-library/2.7/kernlab/libs/kernlab.so': libRlapack.so: cannot open shared object file: No such file or directory Fatal error: unable to

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()

I have never been able to get class probabilities to work and I am relatively new to using these tools, and I am looking for some insight as to what may be wrong. I am using caret with kernlab/ksvm. I will simplify my problem to a basic data set which produces the same problem. I have read the caret vignettes as well as documentation for ?train. I appreciate any direction you can give. I

hello, I have the following problem setting parameter 'frac' in the sigest function of the kernlab package. ## executing the ?sigest example: library(kernlab) data(spam) srange <- sigest(type~.,data = spam) ## works fine... ## setting 'frac' explicitly ## (in this case even to the default of .25) options(error=recover) srange <- sigest(type~.,data = spam, frac = .25) ##

Hi, I am trying to pass initial cluster assignments to the kkmeans method<http://rss.acs.unt.edu/Rdoc/library/kernlab/html/kkmeans.html>of kernlab. It is not clear to me how I can set the parameter *centers* with "initial cluster centers" as stated in the documentation? thanks, --ahmed [[alternative HTML version deleted]]

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the

Hello, together I'm trying to use user defined kernel. I know that kernlab offer user defined kernel(custom kernel functions) in R. I used data spam including package kernlab. (number of variables=58 number of examples =4061) i'm user defined kernel's form, kp=function(d,e){ as=v*d bs=v*e cs=as-bs cs=as.matrix(cs) exp(-(norm(cs,"F")^2)/2) }

I use the svm package kernlab .I have two question. In R library(kernlab) m=ksvm(xtrain,ytrain,type="C-svc",kernel=custom function, C=10) alpha(m) alphaindex(m) I can get alpha value and alpha index about package. 1. Assumption that number of sample are 20. number of support vectors are 15. then rest 5`s alphas are 0? 2. I want use kernelMatrix xtrain=as.matrix(xtrain)

Hi useR's, I am resending this request since I got no response for my last post and I am new to the list so pardon me if I am violating the protocol. I am trying to use the "Kernlab" package for training and prediction using SVM's. I am getting the following error when I am trying to use the predict function: > predictSvm = predict(modelforSVM, testSeq); Error in

kernlab version 0.9-7 is now online and incorporates : + a much improved fast implementation of string kernels "stringdot" based on suffix arrays. + a new kernel method kmmd() which implements a non-parametric kernel based two sample test. The new kernlab version also includes many minor improvements and fixes. Alexandros _______________________________________________

Hi, I found a small problem with kernlab. The problem, I think, is that the 3-fold cross-validation performed to estimate probabilities is not class-balanced, so the classifier could find empty classes. The following example (maybe a little forced) show the error: data(glass) set.seed(1)