similar to: Question about ROCR package

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")

Hi, I understand for 1 d classifiers, you can use ROCR package. Is there a package you can plot ROC curve for 2d classifiers? One of my colleagues asked me about this. I have been quite puzzled, conceptually, how you can do the ROC curve for 2d classifiers. Can someone share his/her knowledge or experience? Thanks in advance. -- Waverley @ Palo Alto

Dear R-philes, I am plotting ROC curves for several cross-validation runs of a classifier (using the function below). In addition to the average AUC, I am interested in obtaining a confidence interval for the average AUC. Is there a straightforward way to do this via the ROCR package? plot_roc_curve <- function(roc.dat, plt.title) { #print(str(vowel.ROC)) pred <-

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 have 5 markers that can be used to detect an infection in combination. Could you please advise me how to use functions in ROCR/ other package to produce the ROC curve for a combination of markers?   I have used the following to get ROC statistics for each marker. pred <- prediction(y$marker1, y$infectn) perf <-performance(pred,"tpr","fpr")

Dear all, I try to compare the performances of several parameters to diagnose lameness in dogs. I have several ROC curves from the same dataset. I plotted the ROC curves and calculated AUC with the ROCR package. I would like to compare the AUC. I used the following program I found on R-help archives : From: Bernardo Rangel Tura Date: Thu 16 Dec 2004 - 07:30:37 EST

Hi, In perl, to get a substring matching a particular pattern can be implemented like the following example: $x = "AAAA.txt"; if ($x=~ /(.*?)\.txt/){ $prefix = $1; } So how to do the same thing in R? Can someone provide me the code sample? Thanks much in advance. -- Waverley @ Palo Alto

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(

Hi, Can someone help as how to use R to program google search in the R code? I know that other languages can allow or have the google search API If someone can give me some links or sample code I would greatly appreciate. Thanks. -- Waverley @ Palo Alto

Please help. I found the code in the archive. The author of this script says: "The first function (seROC) calculate the standard error of ROC curve, the second function (cROC) compare ROC curves." Can some one explain to me what are the na, nn and r parameters which are used as the input to the following two functions? Thanks much in advance. > From: Bernardo Rangel Tura >

Hello, I would like to use the ROCR package to draw ROC curves and compute AUC values. However, in the specific context of my application, the true positive rates and false positive rates are already provided by some upstream method. Of course, I can draw a ROC plot with the following command : plot(x=FPrate, y=TPrate, "o", xlab="false positive rate", ylab="true

Dear List, I am trying to assess the prediction accuracy of an ordinal model fit with LRM in the Design package. I used predict.lrm to predict on an independent dataset and am now attempting to assess the accuracy of these predictions. >From what I have read, the AUC is good for this because it is threshold independent. I obtained the AUC for the fit model output from the c score (c =

Dear Colleagues, I am looking for a package or previous implemented R to subgroup and exaustively divide a vector of squence into 2 groups. For example: 1, 2, 3, 4 I want to have a group of 1, (2,3,4) (1,2), (3,4) (1,3), (2,4) (1,4), (2,3) (1,2,3), 4 (2,3), (1,4) ... Can someone help me as how to implement this? I get some imaginary problem when the sequence becomes large. Thanks much in

*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]]

Hi, I have a matrix a = matrix (1:16, 4, 4) b = c (2,3) I want to find out which rows of a, where a[,1] equals any values of b? I know that if b is only one value, e.g, b=2, then what I want is a[a[,1] == 2,] But what about if it is not one value but a vector of values? Thanks much in advance. -- Waverley @ Palo Alto

Hi, I have a question of aov. e.g. aov.ex = aov(x~y) summary(aov.ex) The aov summary will print to the screen. How can I extract the aov result, in particular the values of Pr(>F) and F value into a vector so that I can use them for other use? Thanks. -- Waverley @ Palo Alto

Hi, I am using the function heatmap(stats) to draw a microarray heatmap, columns are samples and rows are gene features. I did a 2D clustering during the heatmap drawing. The features and samples indeed cluster into several blocks both vertically and horizontally. I can get the index of re-ordered rows and columns after the heatmap drawing by typing the the return variable of the heatmap

I might be missing something but I thought that AUC was a measure for comparing ROC curves, so there is nothing else needed to "compare" them. The larger AUC is the higher correlation of 2 variables compared. No other measures or calculations are needed. Jarek Tuszynski -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On

Hi, there. Suppose I already have sensitivities and specificities. What is the quick R-function to calculate AUC for the ROC plot? There seem to be many R functions to calculate AUC. Thanks. Yulei [[alternative HTML version deleted]]

Hello Is there an easy way, i.e. a function in a package, to calculate the area under the curve (AUC) for drug serum levels? Thanks for any advice -- Armin Goralczyk, M.D. -- Universit?tsmedizin G?ttingen Abteilung Allgemein- und Viszeralchirurgie Rudolf-Koch-Str. 40 39099 G?ttingen -- Dept. of General Surgery University of G?ttingen G?ttingen, Germany -- http://www.chirurgie-goettingen.de