similar to: AUC, C-index and p-value of Wilcoxon

Sorry, I have wrote a wrong subject in the first email! Regards, Linda ---------- Forwarded message ---------- From: linda Porz <linda.porz@gmail.com> Date: 2011/5/25 Subject: combined odds ratio To: r-help@r-project.org Cc: r-help-request@stat.math.ethz.ch Dear all, I am looking for an R function which does stepwise selection cox model in r (delta chisq likelihood ratio test) similar

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

Hi all, I have found the following function online median.test<-function(y1,y2){ z<-c(y1,y2) g <- rep(1:2, c(length(y1),length(y2))) m<-median(z) fisher.test(z<m,g)$p.value } in http://www.mail-archive.com/r-help@r-project.org/msg95278.html I have the following data > group1 <- c(2, 2, 2, 1, 4, 3, 1, 1) > group2 <- c(3, 1, 3, 1, 4, 1, 1, 1, 7, 1, 1, 1, 1,

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]]

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

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

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 =

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 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 <-

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

Hallo! Does anybody know a way to calculate the sample size for comparing AUC of ROC curves against 'by chance' with AUC=0.5 (and/or against anothe AUC)? Thanks! Karl

I am running my code in a loop and it does not work but when I run it outside the loop I get the values I want. n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta <- 0 ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit))

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

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be

Hello R-listers, I'm working in an experiment that try to determine the degree of infection of different clones of a fungus and, one of the measures we use to determine these degree is the counting of antibodies in the plasma at different dilutions, in this experiment the maximum number of dilutions was eleven. I already checked for differences on the maximum concentration of the

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 all, does anyone know why the area under the curve (AUC) is negative? I'm using ROC function with a logistic regression, package Epi. First time it happens... Thanks a lot! Bruno -- View this message in context: http://r.789695.n4.nabble.com/ROC-Curve-negative-AUC-tp4650469.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I have a probability of presence of distribution of a species of interest (varying from 0 to 1 in continuous form) and I have a set of points where I know that species really occurs. But I don´t have points of absence. So, for each true presence I know the estimated presence. I would like to know how can I compute AUC, taking account these Available data. Best wishes,

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