similar to: Help on calculating AUC with caTools trapz(a,b) command

Hi I am trying to perform a bootstrap estimate of classification accuracy of a logistic regression using the 'Daim' package in r using the code at the bottom of this post, this all works great and I get the .632+ misclassification accuracy, specificity, sensitivity, AUC etc etc but what I would like is to access the list of AUC for each of the bootstrap samples as I need calculate the 95%

Hi, I was lately debugging parts of my 'colAUC' function in caTools package, and in a process looked into other packages for calculating Areas Under ROC Curves (AUC). To my surprise I found at least 6 other functions: * wilcox.test * AUC from ROC package, * performance from ROCR package, * auROC from limma package, * ROC from Epi package, * roc.area from verification

I am doing a simulation with a relatively large data set (20,000 observations) for which I want to calculate the area under the Receiver Operator Curve (AUC) for many parameter combinations. I am using the ROC library and the following commands to generate each AUC: rocobj=rocdemo.sca(truth = ymis, data = model$fitted.values, rule = dxrule.sca) #generation of observed ROC object

Dear listers, I am trying to create a RasterLayer of the values of a rasterbrick object. The rasterbrick object has, for example, 100cells library(raster) r <- raster(ncol=10, nrow=10) r[]=1:ncell(r) s <- brick(r,r,r) s <- s * 1:3 Each cell of the rasterfinal will have the AREA UNDER CURVE formed by the values in each cell of the three original rasters. For example: s[4] has the

I could do this in various hacky ways, but what's the right way? I have a nice application of the by function, which does what I want. The output looks like this: > auc_stress lab.samples.stress$subid: 2 cortisol amylase 1 919.05 6834.8 ---------------------------------------------------------------------------------------------------------------------------

Hi, I need to apply the Partial AUC in ROC curves. How I do this?. And I need the SE of partial AUC too. I have been reading ?caTool, ?Epi, ?verification, ?ROC, ?ROCR, ?genefilter and more. I only see the partial AUC, but I do not the SE. Thank you, if you can help me. Xavier G. Ordo?ez Doctoral Student

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(

Hallo, I modified a code given by Andrija, a contributor in the list  to achieve two objectives: create 1000 samples from a list of 207 samples with each of the samples cointaining 20 good and 20 bad. THis i have achievedcalcuate AUC each of the 1000 samples, this i get an error. Please see the code below and assist me. > data<-data.frame(id=1:(165+42),main_samp$SCORE,

Dear R-helper, I am working with ROCR of Tobias Sing et. al. to compare the performances of logistic and nnet models on a binary response. I had the performance plots, but I have problem finding out other performance statistics (eg. MSE/ASE, AUC). Any help on this? Thanks Ilham [[alternative HTML version deleted]]

Hi, I would like to compare differences in AUC from 2 different models, glm and gam for predicting presence / absence. I know that in theory the model with a higher AUC is better, but what I am interested in is if statistically the increase in AUC from the glm model to the gam model is significant. I also read quite extensive discussions on the list about ROC and AUC but I still didn't find

Dear all, I was wondering if someone can help me. I am learning SVM for classification in my research with kernlab package. I want to know about classification performance using Area Under Curve (AUC). I know ROCR package can do this job but I found all example in ROCR package have include prediction, for example, ROCR.hiv {ROCR}. My problem is how to produce prediction in SVM and to find

Hello, Is there is any R function computes the AUC for paired data? Many thanks, Samuel [[alternative HTML version deleted]]

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 =

Hi there, I really tried hard to understand and find my own solution, but now I think I have to ask for your help. I already developed some script code for my problem but I doubt that it is correct. I have the following problem: Image you develop a logistic regression model with a binary outcome Y (0/1) with possible preditors (X1,X2,X3......). The development of the final model would be

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 <-

Hello - I am using glmnet to generate a model for multiple cohorts i. For each i, I run 5 separate models, each with a different x variable. I want to compare the fit statistic for each i and x combination. When I use auc, the output is in some cases is < .5 (.49). In addition, if I compare mean MSE (with upper and lower bounds) ... there is no difference across my various x variables, but

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 all, I am using the ROCR library to compute the AUC and also the Hmisc library to compute the C-index of a predictor and a group variable. The results of AUC and C-index are similar and give a value of about 0.57. The Wilcoxon p-value is <0.001! Why the AUC is showing small value and the p-value is high significant? The AUC is based on Wilcoxon calculation? Many thanks, Lina

Hi, I have a dataset (see attached) with 2 variables "Y" is binary, "x" is a continuous variable. I want to calculate area under the curve (AUC) for the ROC curve, but I got different AUC values using ROC() from Epi package vs. rcorr.cens() from rms package: test<-read.table("test.txt",sep='\t',header=T,row.names=NULL) y<-test$y x<-test$x

Dear all I am trying to compare the performances of several methods using the AUC0.1 and not the whole AUC. (meaning I wanted to compare to AUC's whose x axis only goes to 0.1 not 1) I came to know about the Mcneil Hanley test from Bernardo Rangel Tura and I referred to the original paper for the calculation of "r" which is an argument of the function cROC. I can only find the