similar to: Predictions from a logistic regression model with validation for ROCR

Dear developers, I have jutst started working with caret and all the nice features it offers. But I just encountered a problem: I am working with a dataset that include 4 predictor variables in Descr and a two-category outcome in Categ (codified as a factor). Everything was working fine I got the results, confussion matrix etc. BUT for obtaining the AUC and predicted probabilities I had to add

Hi, First let me thank you for the incredible help and resource that this forum is. I am trying to compare the repeated measurement of more than 100 analytes that have been take in 70 subjects at 2 time points adjusted for the time difference of sample times(TimeDifferenceDays), therefore I wanted to do it with a function that allows me to do all at once. (131 is the column difference that

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

I´ve several packages for latent class analysis, but I was wondering if there is a package for continuos variables, which allows latent prfile analysis. Thanks for your help in advance, J Toledo _________________________________________________________________ cial-network-basics.aspx?ocid=PID23461::T:WLMTAGL:ON:WL:en-xm:SI_SB_1:092 [[alternative HTML version deleted]]

Dear R Users, R Core Team, I have a two dimensional space where I measure a numerical value in two situations at different points. I have measured the change and I would like to test if there are areas in this 2D-space where there is a different amount of change (no change, increase, decrease). I don´t know if it´s better to analyse the data just with the coordinates or if its better to group them

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 Wiza[R]ds, I have a data.frame header that looks like this: v2FfaPre15 v2FfaPre10 v2FfaPre5 v2Ffa2 v2Ffa3 v2Ffa4 I need it to look like this, 15 10 5 2 3 4 i.e., with v2FfaPre and v2Ffa stripped off Any suggestions, Thanks in advance! -- Oscar Oscar A. Linares, MD Translational Medicine Unit LaPlaisance Bay, Bolles Harbor Monroe, Michigan 48161

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 <-

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