similar to: Extract information from S4 object

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

Dear list I'm testing a predictor and I produced nice performance plots with ROCR package utilizing the 3 standard command pred <- prediction(predictions, labels) perf <- performance(pred, measure = "tpr", x.measure = "fpr") plot(perf, col=rainbow(10)) The pred object and the perfo object are S4 with the following slots An object of class "performance"

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, *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]]

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

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 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, 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 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

Hello All,  I have used logistic regression glm in R and I am evaluating two models both learned with glm but with different predictors. model1 <- glm (Y ~ x4+ x5+ x6+ x7, data = dat, family = binomial(link=logit))model2 <- glm (Y~ x1 + x2 +x3 , data = dat, family = binomial(link=logit))  and I would like to compare these two models based on the prediction that I get from each model: pred1 =

Hello, I was unable to find clues to my problem in ?optim. Using the data and code below, I get an error ("system is exactly singular") when a particular line of code is left in, but have found that 'optim' works when I comment it out. The line of code in question is after the closeAllConnections() line of code and contains a call to "na.approx" from the zoo package.

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

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 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 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 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")

Hola! Tengo que hacer una curva ROC com unos datos que obtuve de hacer una macro de excel y aplicar unas reglas, y basicamente tengo que a partir de la variacion del tiempo y la velocidad del barco obtengo diferentes porcentajes de true positives (TP) y false positives (FP) y con eso deberia de construir una curva ROC. Dada mi ignorância en este tema, no se muy bien por donde empezar , estuve

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'm in the process of converting some S3 methods to S4 methods. I have this function : setGeneric("enrichmentCalc", function(rs, organism, seqLen, ...){standardGeneric("enrichmentCalc")}) setMethod("enrichmentCalc", c("GenomeDataList", "BSgenome"), function(rs, organism, seqLen, ...) { ... ... ... })

Hola, Marta, Por lo que he podido ver tus datos ya tienen calculados las tasas de verdaderos y falsos positivos (TPR y FPR). También parece que los tienes ordenados por la variable FishSpeed y supongo que también por las que parecen marcas de tiempo. No necesitas ROCR porque con un simple plot te sale algo parecido a una curva. Eso sí, veo que son medidas repetidas en el tiempo para cada valor de