similar to: ROCR data input

I'm trying to use ROCR to create a simple cutoff vs recall plot (recall at p) on the example ROCR.simple dataset: library(ROCR) data(ROCR.simple) pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels) perf <- performance(pred, "rec") plot(perf) But R crashes on me on the last line. I'm using R 2.14.1, ROCR 1.0-4. ?Any ideas? Thanks in advance. -- Yang Zhang

Dear R-help and ROCR developers (Tobias Sing and Oliver Sander) - I think my first question is generic and could apply to many methods, which is why I'm directing this initially to R-help as well as Tobias and Oliver. Question 1. The plot function in ROCR will average your cross validation data if asked. I'd like to use that averaged data to find a "best" cutoff but I

Hi, I have an object from the prediction function from the ROCR package and I would like to extract one of the slots from the object, for example the cutoffs slot. However the usual techniques ($, [["name"]]) of subsetting don't work. How can I assess the lists in the slots? Here is an example of what I am working with: library(ROCR) data(ROCR.simple) pred <-

Dear List,   I am new to ROC analysis and the package ROCR. I want to compute the confidence intervals of sensitivity and specificity for a given cutoff value. I have used the following to calculate sensitivity and specificity:   data(ROCR.simple) pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)   se.sp <- function (cutoff, performance) {     sens <-

Hi everybody, I am having a problem building a ROC curve with my data using the ROCR package. I have 10 lists of proteins such as attached (proteinlist.xls). each of the lists was calculated with a different p-value. The goal is to find the optimal p-value for the highest number of true positives as well as lowaest number of false positives. As far as I understood the explanations from the

Packages pROC and ROCR both calculate/approximate the Area Under (Receiver Operator) Curve. However the results are different. I am computing a new variable as a predictor for a label. The new variable is a (non-linear) function of a set of input values, and I'm checking how different parameter settings contribute to prediction. All my settings are predictive, but some are better. The AUC i

I meet lots of problem when installing the package ROCR, do you have meet such problems? 1, biocLite("ROCR") 2, biocLite("gplots") 3, biocLite("Rgraphviz") 4, sudo apt-get install graphviz oh, no, unlimited question, what's wrong with R in ROCR or gplots or et al Error : object ‘nobs’ is not exported by 'namespace:gdata' installation of package

Hello all, I have two problems with the ROCR package. First Problem: the add=TRUE option does not work for plotting performance objects The following code is taken from the reference manual (example for ROCR.hiv, page2) data(ROCR.hiv) attach(ROCR.hiv) pred.svm <- prediction(hiv.svm$predictions, hiv.svm$labels) perf.svm <- performance(pred.svm, 'tpr', 'fpr') pred.nn <-

Dear R users, we are glad to announce the release of our new R package ROCR, for visualizing the performance of scoring classifiers (available on CRAN). We hope that the package might be useful for those of you working on classification problems. For details, see the package description below, or the ROCR website: http://rocr.bioinf.mpi-sb.mpg.de. You can get a short overview by typing

Dear R users, we are glad to announce the release of our new R package ROCR, for visualizing the performance of scoring classifiers (available on CRAN). We hope that the package might be useful for those of you working on classification problems. For details, see the package description below, or the ROCR website: http://rocr.bioinf.mpi-sb.mpg.de. You can get a short overview by typing

If we use the ROCR package to find the accuracy of a classifier pred <- prediction(svm.pred, testset[,2]) perf.acc <- performance(pred,"acc") Do we?find the maximum accuracy?as follows?(is there a simplier way?): > max(perf.acc at x.values[[1]]) Then to find the cutoff point that maximizes the accuracy?do we do the following?(is there a simpler way): > cutoff.list <-

When I try to install the ROCR package (which requires gplots) on Ubuntu 9.10 (Xubuntu Karmic Koala) 64-bit on R version 2.9.2 (2009-08-24), it eats up all my RAM (2GB) and swap (4GB) and keeps allocating more memory until Linux's out of memory (OOM) killer kills the perl process. This problem is special to Ubuntu because I can install other packages (such as party) on this Ubuntu system, and

Hi, I don't really understand how ROCR works. Here's another example with a randomforest model: I have the training dataset(bank_training) and testing dataset(bank_testing) and I ran a randomForest as below: bankrf<-randomForest(y~., bank_training, mtry=4, ntree=2, keep.forest=TRUE,importance=TRUE) bankrf.pred<-predict(bankrf, bank_testing)

I am trying to use package ROCR to analyze classification accuracy, unfortunately there are some problems right at the beginning. Question 1) When I try to run demo I am getting the following error message > library(ROCR) > demo(ROCR) > if(dev.cur() <= 1) .... [TRUNCATED] Error in get(getOption("device")) : wrong first argument When I issue the command > dev.cur() it

Hi, My questions concerns the ROCR package and I hope somebody here on the list can help - or point me to some better place. When evaluating a model's performane, like this: pred1 <- predict(model, ..., type="response") pred2 <- prediction(pred1, binary_classifier_vector) perf <- performance(pred, "sens", "spec") (Where "prediction" and

Hi, I am using logistic regression to classify a binary psychometric data. using glm() and then predict.glm() i got the predicted odds ratio of the testing data. Next i am going to plot ROC curve for the analysis of my study. Now what i will do: 1. first select a cut-off (say 0.4) and classify the output of predict.glm() into {0,1} segment and then use it to draw ROC curve using ROCR package

Hi, I'm involved in a bioinformatics project at my university, and we're doing a comparison paper between some methods of classification of nc-RNA. I've been encharged of ploting the ROC curves' graphs. But I'm new on working with R and I'm having some difficulty with the prediction-class. I don't get where the values of ROCR.simple$predictions, for example, came from

Dear R users, Hi. I am using prediction function in ROCR package. y consists of two classes 0 and 1. However, since I am using cross-validation, a sampled small number of y may consist of only one class >y [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 In this case, prediction function gives an error: Error in prediction(predic, y) : Number of classes is not equal to 2. ROCR currently supports

Dear useRs, an update of the ROCR package is available on CRAN. ROCR helps in evaluating the performance of scoring classifiers using ROC graphs, precision/recall plots, lift charts and many other performance metrics. For further information check http://rocr.bioinf.mpi-sb.mpg.de and http://bioinformatics.oxfordjournals.org/cgi/reprint/21/20/3940 NEWS: - added an optional parameter

Dear useRs, an update of the ROCR package is available on CRAN. ROCR helps in evaluating the performance of scoring classifiers using ROC graphs, precision/recall plots, lift charts and many other performance metrics. For further information check http://rocr.bioinf.mpi-sb.mpg.de and http://bioinformatics.oxfordjournals.org/cgi/reprint/21/20/3940 NEWS: - added an optional parameter