similar to: time-dependent ROC package

Hi everyone, I am doing 5 years mortality predictive index score with survival analysis using a Cox proportional hazard model where I have a continous predictive variable and a right censored response which is the mortality, and the individuals were followed a maximum of 7 years. I'd like to asses the discrimination ability of survival analysis Cox model by computing a ROC curve and area

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

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,

Hello I am trying to make a graph of 10 different lines built each from 4 different segments and to add a darker line that will represent the average of all graphs - all in the same plot.Actually each line is a ROC plot The code I'm using for plotting one line is as follows: logit.roc.plot <- function(r, title="ROC curve") { old.par <- par(no.readonly = TRUE);

I'm using randomSurvivalForest to predict survival from a rather small sample. As it's not enough data to have training and validation set, I rely on the "Estimate of error rate" computed by the randomForest. If I understand the method correctly, it repeatedly partitions the data into varying training/validation sets during the learning steps, which also produces the

> 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

Hello I am trying to make a graph of 10 different lines built each from 4 different segments and to add a darker line that will represent the average of all graphs - all in the same plot.Actually each line is a ROC plot The code I'm using for plotting one line is as follows: logit.roc.plot <- function(r, title="ROC curve") { old.par <- par(no.readonly = TRUE);

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 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

Hello all, I am very new to R and i am facing two problems. First i didn't succeed changing the konsole language in english even after trying the line command set language='en'. I would like to plot ROC curves. I have a serie of 10 threshold tests that i do for 10 patients. The prediction for the patients is always the same but the status can change given to the considered threshold.

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

if nobody knows the answer to my question on time-dependent ROC (see below), does any body know how TP and FP are related to classes and samples (orders) in non-time dependent ROC? Any reference? Thanks --- On Tue, 11/3/09, carol white <wht_crl at yahoo.com> wrote: > From: carol white <wht_crl at yahoo.com> > Subject: ROC > To: r-help at stat.math.ethz.ch > Date:

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

Hello all, Trying to get this piece of code to work on my data set. It is from http://www.itc.nl/personal/rossiter. logit.roc <- function(model, steps=100) { field.name <- attr(attr(terms(formula(model)), "factors"), "dimnames")[[1]][1] eval(parse(text=paste("tmp <- ", ifelse(class(model$data) == "data.frame", "model$data$",

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, Dear all R users: Does someone know whether R can calculate the Receiver Operating Characteristic (ROC) Curves? I didn't find it from the packages. Thanks a lot. --- Xin

Hi, Is there any package or code to compare and display correlated ROC curves in R? Thanks, Reza [[alternative HTML version deleted]]

Hello, I am comparing two ROC curves with bootstraping. However, some runs return "p-value = NA," and I have no clue why. Is this anyhow related to like sample size or no sufficient numbers of bootstraping? I used the default value (i.e. boot.n=2000), and the number of observations are quite big since I am comparing maps (e.g., the largest has more than 9 million observations).