similar to: Roc curves confidence intervals

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, I don't really understand how ROCR works. Here's another example with a randomforest model: I have the training dataset(bank_training) and testing dataset(bank_testing) and I ran a randomForest as below: bankrf<-randomForest(y~., bank_training, mtry=4, ntree=2, keep.forest=TRUE,importance=TRUE) bankrf.pred<-predict(bankrf, bank_testing)

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

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'm new to stats and R, so can you please help me or guide me building ROC curve in an elaborate way with codes I loaded ROCR package, but I'm not sure how to use it. Requirement To build ROC curve using only PSA(variable) alone of the original cohort against the ROC of the Model of the original cohort. It would be really great if you could help me with this. Thanks

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),

Hola! Tengo que hacer una curva ROC com unos datos que obtuve de hacer una macro de excel y aplicar unas reglas, y basicamente tengo que a partir de la variacion del tiempo y la velocidad del barco obtengo diferentes porcentajes de true positives (TP) y false positives (FP) y con eso deberia de construir una curva ROC. Dada mi ignorância en este tema, no se muy bien por donde empezar , estuve

Hi, I understand for 1 d classifiers, you can use ROCR package. Is there a package you can plot ROC curve for 2d classifiers? One of my colleagues asked me about this. I have been quite puzzled, conceptually, how you can do the ROC curve for 2d classifiers. Can someone share his/her knowledge or experience? Thanks in advance. -- Waverley @ Palo Alto

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

Dear List,   I am new to ROC analysis and the package ROCR. I want to compute the confidence intervals of sensitivity and specificity for a given cutoff value. I have used the following to calculate sensitivity and specificity:   data(ROCR.simple) pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)   se.sp <- function (cutoff, performance) {     sens <-

Hi everybody, I have a data set with a value and a status (positive or negative case) and I want make a ROC Analysis. So, with ROCR Package, I have got the ROC curve (True Positive Fraction [tpf] according 1-True Negative Fraction [1-tnf]). http://r.789695.n4.nabble.com/file/n4469203/01.png But, now I want a new graphic which show the sum of true positive fraction and true negative fraction

Hi, i have a fairly large amount of genomic data. I have created a dataframe which has "Reference" as one column and "Variation" as another. I want to plot a ROC curve based on these 2 columns. I have serached the R manual but I could not understand. Can anybody help me with the R code for plotting ROC curve. Thnx ashu6886 -- View this message in context:

Hi, I need to build ROC curve in R, can you please provide data steps / code or guide me through it. Thanks and Regards Rithesh M Mohan [[alternative HTML version deleted]]

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.

I use ROCR to plot multiple runs' performance. Using the sample code as example: # plot ROC curves for several cross-validation runs (dotted # in grey), overlaid by the vertical average curve and boxplots # showing the vertical spread around the average. data(ROCR.xval) pred <- prediction(ROCR.xval$predictions, ROCR.xval$labels) perf <- performance(pred,"tpr","fpr")

Hi everybody, I am having a problem building a ROC curve with my data using the ROCR package. I have 10 lists of proteins such as attached (proteinlist.xls). each of the lists was calculated with a different p-value. The goal is to find the optimal p-value for the highest number of true positives as well as lowaest number of false positives. As far as I understood the explanations from the

Hola, Marta, Por lo que he podido ver tus datos ya tienen calculados las tasas de verdaderos y falsos positivos (TPR y FPR). También parece que los tienes ordenados por la variable FishSpeed y supongo que también por las que parecen marcas de tiempo. No necesitas ROCR porque con un simple plot te sale algo parecido a una curva. Eso sí, veo que son medidas repetidas en el tiempo para cada valor de

*Hi, *Does anyone know how can I show an *ROC curve for R-SVM*? I understand in R-SVM we are not optimizing over SVM cost parameter. Any example ROC for R-SVM code or guidance can be really useful. Thanks, Angel. [[alternative HTML version deleted]]

All, I'm trying again with a slightly more generic version of my first question. I can extract the plotted values from hist(), boxplot(), and even plot.randomForest(). Observe: # get some data dat <- rnorm(100) # grab histogram data hdat <- hist(dat) hdat #provides details of the hist output #grab boxplot data bdat <- boxplot(dat) bdat #provides details of the boxplot

HI, Dear R community, I want to know how to select the optimal decision threshold from the ROC curve? At what threshold will give the highest accuracy? Thanks! -- Sincerely, Changbin -- [[alternative HTML version deleted]]