similar to: Recursive-SVM (R-SVM)

Hello, I'm using recursive SVM script (rSVM - http://www.stanford.edu/group/wonglab/RSVMpage/R-SVM.html ) on some microarray data. The data to be input are log2, as numeric matrix w/ attributes -- str(svm_num_mat) num [1:10, 1:12340] 13.1 13.1 13.1 13.1 13.0 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:10] "rma_log2_con_sample_1"

*Hi, *Does anyone know how can I show an *ROC curve for R-SVM*? I understand in R-SVM we are not optimizing over SVM cost parameter. Any example ROC for R-SVM code or guidance can be really useful. Thanks, Angel. [[alternative HTML version deleted]]

The VQ codebook generation utilities are more or less ready to go. Minor things are left to do, but those will have to be handled as I to them :-) The next thing to do is complete the interface to get compression modes and codebooks to the library for encoding (minor stuff), and then arrange how exactly the MDCT residue is filtered through the codebooks. VQ codebook util instructions:

> library(ROCR) > library(e1071) svmres.prob <- svm(traindx, traindy, probability=TRUE) svmpred.prob <- predict(svmres.prob, testdx, probability = TRUE, decision.values = TRUE, type="prob") > print(length(attr(svmpred.prob, "probabilities"))) [1] 0 > print(attr(svmpred.prob, "probabilities")) NULL > print(attributes(svmpred.prob)$decision.values)

Hi R helpers! I'm still a bit ( alot) confused by the use of "inverseTrans" and "ytype" in areg.boot (Hmisc): What I want to do seems very simple, but I do not get the result I want: plot the predicted values in the original scale. (I did not understand the documentation, sorry!) for instance the following code