similar to: Problems when install ROCR

Dear All, Can I use lm() to fit more than one response in single expression. e.g data is a matrix of these variables R1 R2 R3 X1 X2 X3 1 2 1 1 2 3 Now i wnat to fit R1~X1 R2~X2 R3~X3 in turn, and I don't want to do it use loops of couse it it easy to make it using loops,but the proceed is very slow since the data is very big Can't anybody give me some tips or help, my eamil:

Hi, all There are 62 samples in my data and I tested 3 times for each one, then I want to use ICC(intraclass correlation) from irr package to test the consistency among the tests. *combatexpdata_p[1:62] is the first text results and combatexpdata_p[63:124] * is the second one and *combatexpdata_p[125:186]* is the third. Here is the result:

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

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, 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 <-

I have tried installing the package (ROCR) with this command: Install.packages(ROCR) And with this command on the command line R CMD INSTALL ROCR_1.0-4.tar.gz But both times I get exactly the same error shown below, I don't understand what is wrong, is this an error in the package code? Thank you Philip probinson@bioinform08:/tmp/RtmpO0rFbx/downloaded_packages$ R CMD

All, I'm trying again with a slightly more generic version of my first question. I can extract the plotted values from hist(), boxplot(), and even plot.randomForest(). Observe: # get some data dat <- rnorm(100) # grab histogram data hdat <- hist(dat) hdat #provides details of the hist output #grab boxplot data bdat <- boxplot(dat) bdat #provides details of the boxplot

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

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 <-

I use ROCR to plot multiple runs' performance. Using the sample code as example: # plot ROC curves for several cross-validation runs (dotted # in grey), overlaid by the vertical average curve and boxplots # showing the vertical spread around the average. data(ROCR.xval) pred <- prediction(ROCR.xval$predictions, ROCR.xval$labels) perf <- performance(pred,"tpr","fpr")

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 <-

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

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)

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

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

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

Hi there, I'm having some difficulty with the ROCR package. I've installed it fine, and the sample data works (ROCR.simple), however when I try to load my own data it complains that there is an error in prediction as the number of classes is not equal to 2. I read the data from a text file which contains one column of probabilities and one column of binary 0 and 1. I then put it into a

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 am quite new to R and I have two questions regarding ROCR. 1. I have tried to understand how to extract area-under-curve value by looking at the ROCR document and googling. Still I am not sure if I am doing the right thing. Here is my code, is "auc1" the auc value? " pred1 <- prediction(resp1,label1) perf1 <- performance(pred1,"tpr","fpr") plot(