similar to: Error with Package "Kernlab" for SVM prediction

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

Dear useR's I have a small basic problem which I am hoping to get some help with. I have a data frame, testSeq_df, with 1 row and 500 columns. Each column is a character (a,c,g or t). I want this sequence to have 4 factors (a,c,g,t). When I try the following: for(i in 1:500){ if (length(levels(testSeq_df[,i]))==1) levels(testSeq_df[,i]) <-

Hi useR's, Has anyone used the "Lars" package in R before? If so, is there any tutorial (not manual) or worked out example online for this R package that one can go through to figure out how one can use this package with lasso regression? I appreciate any help I can get in this direction. Sincerely, Vishal -- *Vishal Thapar, Ph.D.* *Scientific informatics Analyst Cold Spring

Hey guys, I have what i think is a really simple problem :( I installed the seqinr library. I want to do an RSCU analysis. But i can't get it to work in even the simplest case. for example, if i have a string read in: > newdata5 $testseq [1] "agtgagatgatagatagatagatagatagatagatagaccccccagata" and then i perform an RSCU analysis on it... >

> Your patch looks very clean. Some comments: Heh, Jakob was faster :) > - I think you have some literal tabs in your instruction descriptions. The tabs can be seen in some other places as well. Also, there is a "mix" of coding conventions in the files. It will be really nice to use only one :) > - Your tests are nice, but you could use some more of them. I would recommend

Hi there! I'm having some strange permissions issues with one of my systems that's on an Active Directory domain. Here's the basic background: - System is joined to AD domain. Users authenticate fine via Kerberos, and are authorized via an AD user group. They can browse the share, create files, etc. without incident. "valid users" lets them in. - User information for the

On Jan 30, 2010, at 6:49 AM, Anton Korobeynikov wrote: >> Your patch looks very clean. Some comments: > Heh, Jakob was faster :) I have taken care of everything Jakob mentioned except the extra test cases. I will get to these as soon as I can. > >> - I think you have some literal tabs in your instruction descriptions. > The tabs can be seen in some other places as well.

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 all, I would like to use the train function from the caret package to train a svm with a spectral kernel from the kernlab package. Sadly a svm with spectral kernel is not among the many methods in caret... using caret to train svmRadial: ------------------ library(caret) library(kernlab) data(iris) TrainData<- iris[,1:4] TrainClasses<- iris[,5] set.seed(2)

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 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

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

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 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

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)

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 All, I am using ksvm method in kernlab R package for support vector machines. I learned the multiclass one-against-one svm from training data and using it to classify new datapoints. But I want to update/finetune the 'svm weights' based on some criteria and use the updated svm weights in the predict method framework. I don't know if its possible or not, how do classify new

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",

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

Hello, I'm trying to using ksvm() in the kernlab package to fit a one-class SVC, but I get a strage result on the cross-validation error estimate. For example, consider this code: data(spam) classifier <- ksvm(type~.,data=spam[which(spam[,'type']=='spam'),], type="one-svc",kernel="rbfdot",kpar=list(sigma=0.1),nu=0.05,cross=10) what I get is: >