similar to: library(kernlab) --- unable to load shared library

Dear R community, I am using the R package `interval` in order to perform some modelling tests of the NPMLE convergence in the case of censoring. So all I am doing is drawing a sample from exponential distribution, making it a censored sample and computing the NPMLE of its distribution function. But when run on Linux Calculate 10.4 the program keeps crashing and reporting a segmentation fault

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, 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 all, What would be an efficient way to match rows of a matrix to a vector? ex: m<-matrix(1:9, nrow=3) m [,1] [,2] [,3] [1,] 1 4 7 [2,] 2 5 8 [3,] 3 6 9 ################################# which(m==c(2,5,8)) # I want this to return 2 ###################### Thanks, Sachin [[alternative HTML version deleted]]

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

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

After importing a table with "M" variables and "N" records, I'd like to calculate chi-square statistics, say, between N1, N2; N1, N3, ..., N1, Ni, and then N2, N3, ... N2, Ni, ..., Ni-1, Ni. Two loops should be ok but the manual & online help don't show a systematic way to do so but instead show hard-code examples so users have to type in the names themselves one

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]]

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! 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

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"

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

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,

Dear list, I am using the ksvm function from kernlab as follows: (1) learning > svm.pol4 <- ksvm(class.labs ~ ., data = train.data, prob.model = T, scale = T, kernel = "polydot") (2) prediction > svm.pol.prd4 <- predict(svm.pol4, train.data, type = "probabilities")[,2] But unfortunately, when calling the prediction, once in every 10s of times (using the exact

Hello all, I'm trying to run a gird parameter search for a svm. Therefore I'M using the ksvm function from the kernlab package. ---- svp <- ksvm(Ktrain,ytrain,type="nu-svc",nu=C) ---- The problem is that the optimization algorithm does not return for certain parameters. I tried to use setTimeLimit() but that doesn't seem to help. I suspect that ksvm() calls c code that

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 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)

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.