similar to: ROC: AUC test

After reading a data.frame using, such as a <- read.spss("data.sav") I want to give the column index 'i', or a[i] to a function, which after some calculation, should print out the results to the standard output. I am struggling how to access the data itself, as e.g. sum(a[i]) does not work in this context. In addition I need to know the name of the variable within the

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

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

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 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.

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]]

Dear R users, pROC is a package to compare, visualize, and smooth receiver operating characteristic (ROC) curves. The package provides the following features: * Partial or full area under the curve (AUC) computation * Comparison of two ROC curves (curves and AUC) * Calculating and plotting confidence intervals * Smoothing of the ROC curve * Coordinates extraction ('coords' function).

Dear R users, pROC is a package to compare, visualize, and smooth receiver operating characteristic (ROC) curves. The package provides the following features: * Partial or full area under the curve (AUC) computation * Comparison of two ROC curves (curves and AUC) * Calculating and plotting confidence intervals * Smoothing of the ROC curve * Coordinates extraction ('coords' function).

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 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 <-

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

> 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

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

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,

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,

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

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

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

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

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