similar to: pROC 1.4.3: compare two ROC curves in R

Dear useRs: I use pROC package to compute the bootstrap C.I. of AUC. The command was as follows: roc1<-roc(all$D,all$pre,ci=TRUE,boot.n=200) However, the result was: Area under the curve: 0.5903 95% CI: 0.479-0.7016 (DeLong) Why the C.I. was computed by the Delong Method? Yao Zhu Department of Urology Fudan University Shanghai Cancer Center Shanghai, China [[alternative HTML version

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

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, 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

> On Jun 26, 2017, at 11:40 AM, Brian Smith <bsmith030465 at gmail.com> wrote: > > Hi, > > I was trying to draw some ROC curves (prediction of case/control status), > but seem to be getting a somewhat jagged plot. Can I do something that > would 'smooth' it somewhat? Most roc curves seem to have many incremental > changes (in x and y directions), but my plot

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

Hi Brian, Your underlying dataset for the ROC curve only has 4 unique values for specificity, even though there are 23 elements in the vector, hence the step function nature of the first plot. The default smoothing in the smooth() function is "binormal". You might try one of the other smoothing options to see the result and whether they make visual sense. In the absence of smoothing,

Hi, I was trying to draw some ROC curves (prediction of case/control status), but seem to be getting a somewhat jagged plot. Can I do something that would 'smooth' it somewhat? Most roc curves seem to have many incremental changes (in x and y directions), but my plot only has 4 or 5 steps even though there are 22 data points. Should I be doing something differently? How can I provide a

The name conflicts in Epi and ROC packages (2 'ROC' functions are the problem) cause the following code to work once, but not twice: library(MASS); data(cats); x = cats[,2] y = ifelse(cats[,1]=='F',0,1) library(Epi); ROC(x,y,grid=0)$AUC library(ROC); AUC(rocdemo.sca(y, x, dxrule.sca)) What is the standard way of resolving name conflicts? Ask maintainers to resolve

Hello, I am making some use of ROC curve analysis. I find much help on the mailing list, and I have used the Area Under the Curve (AUC) functions from the ROC function in the bioconductor project... http://www.bioconductor.org/repository/release1.5/package/Source/ ROC_1.0.13.tar.gz However, I read here... http://www.medcalc.be/manual/mpage06-13b.php "The 95% confidence interval for

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.

I am trying to create the roc plot bootstrap method from library(verification), and when I set the plot =both or emp then I get the following error. The roc.plot works fine when the plot is set to binorm. This is my first time using this function in R and am not sure what this error means or how to resolve the issue. It seems to work ok with the example dataset. Is there an option or data

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

I know how to compute the ROC curve and the empirical AUC from the logistic regression after fitting the model. But here is my question, how can I compute the standard error for the AUC estimator resulting form logistic regression? The variance should be more complicated than AUC based on known test results. Does anybody know a reference on this problem? [[alternative HTML version deleted]]

Dear all, I want to measure the goodness of prediction of my linear model. That's why I was thinking about the area under roc curve. I'm trying the following, but I don't know how to avoid the error. Any help would be appreciated. library(ROCR) model.lm <- lm(log(outcome)~log(v1)+log(v2)+factor1) pred<-predict(model.lm) pred<-prediction(as.numeric(pred),

Hello list, I am trying to perform ROC analysis and count the AUC in order to validate my results. I use package ROCR. I would like to count the AUC not under the cutoff found by "performance" but to use another cutoff that I calculate. How could I change the following command in order to get what I want? perform=performance(pred,measure="auc",x.measure="cutoff"),

require(randomForest) rf.pred<-predict(fit, valid, type="prob") > rf.pred[1:20, ] 0 1 16 0.0000 1.0000 23 0.3158 0.6842 43 0.3030 0.6970 52 0.0886 0.9114 55 0.1216 0.8784 75 0.0920 0.9080 82 0.4332 0.5668 120 0.2302 0.7698 128 0.1336 0.8664 147 0.4272 0.5728 148 0.0490 0.9510 153 0.0556 0.9444 161 0.0760 0.9240 162 0.4564 0.5436 172 0.5148 0.4852 176 0.1730

Hi, there: Could someone tell me a simple function of plot ROC curve and calculate AUC in R? My setting is very simple, a column of the true binary response and another column of predicted probabilities. Thanks! Yulei [[alternative HTML version deleted]]

Hi, I'm trying to set up a prediction software, now i testing the performance of my method, so i need to calculate a ROC curve, specially auc, cut-off, sens and spec, i just looking at ROCH package, but it's a mass for me, i'm not a math guy and I'm getting lost Could any of you recommend me an easy-to-use package to do this task? i just have a list of positive/negative samples

Hi users, I need to calculate .632 (and .632+) bootstrap and the cross-validation of area under curve (AUC) to compare my models. Is there any package for the same. I know about 'ipred' and using it i can calculate misclassification errors. Please help. It's urgent. -- View this message in context: