Hi Rasanga, I think there is nothing wrong with that plot (you may compare both curves obtained by ROC from Epi and plot.roc from pROC). As usual, both functions plot (1-specificity) at the x-coordinate against sensitivity on y. Perhaps a bit confusing is the labelling of the latter function, it has 'specificity' on x, but the ticks go (in reading direction) from 100% down to 0% so it is correct - look at the documentation or the source code of that function to fix it to your needs. Anyway - the AUC is (up to changes in sign) invariant to interchanging spec and sens or reversing the values and so is the CI for AUC.
cheers. Am 28.04.2011 19:54, schrieb Rasanga Ruwanthi: > Hi Eik > > It seems plot.roc function below plots sensitivity vs specificity, but > not (1-sensitivity) vs specificity. As you said it gives CIs of AUC, but > is there a way to get it for (1-sensitivity) vs specificity? > > Thanks > Rasanga > > > > --- On *Thu, 28/4/11, Eik Vettorazzi > /<e.vettora...@uke.uni-hamburg.de>/* wrote: > > > From: Eik Vettorazzi <e.vettora...@uke.uni-hamburg.de> > Subject: Re: [R] ROCR for combination of markers > To: "Rasanga Ruwanthi" <ruwanthi_...@yahoo.com> > Cc: "r-help" <r-help@r-project.org> > Date: Thursday, 28 April, 2011, 17:36 > > Hi, > today I accidentally stumbled on a new? package named 'pROC', which > might do the job > > library(pROC) > > out=ROC( form = y1 ~ x + z, plot="ROC") > > #calculate the underlying glm from ROC > prd<-predict(glm(y1 ~ x + z,family="binomial"),type="response") > > #or use the results from ROC > prd<-out$res$lr.eta[-1] > > rocobj <- plot.roc(y1, prd, > main="Confidence intervals", percent=TRUE, > ci=TRUE, # compute AUC (of AUC by default) > print.auc=TRUE) # print the AUC (will contain the CI) > ciobj <- ci.se(rocobj, # CI of sensitivity > specificities=seq(0, 100, 5)) # over a select set of > specificities > plot(ciobj, type="shape", col="#1c61b6AA") # plot as a blue shape > > cheers > > > Am 28.04.2011 17:15, schrieb Rasanga Ruwanthi: > > Many thanks Eik. That was really helpful. I have found ROC > function Epi > > package draws ROC curve for a combination of markers. What I tried was > > > > library(Epi) > > x <- rnorm( 100 ) > > z <- rnorm( 100 ) > > w <- rnorm( 100 ) > > tigol <- function( x ) 1 - ( 1 + exp( x ) )^(-1) > > y1 <- rbinom( 100, 1, tigol( 0.3 + 3*x + 5*z + 7*w ) ) > > out=ROC( form = y1 ~ x + z, plot="ROC",MI=FALSE) > > > > But this function does not produce SE or CI of the AUC or any other > > statistics. Any suggestion to get these? > > > > Thanks again > > Rasanga > > > > > > --- On *Thu, 28/4/11, Eik Vettorazzi > > /<e.vettora...@uke.uni-hamburg.de > > <http://uk.mc1120.mail.yahoo.com/mc/compose?to=e.vettora...@uke.uni-hamburg.de>>/* > wrote: > > > > > > From: Eik Vettorazzi <e.vettora...@uke.uni-hamburg.de > > <http://uk.mc1120.mail.yahoo.com/mc/compose?to=e.vettora...@uke.uni-hamburg.de>> > > Subject: Re: [R] ROCR for combination of markers > > To: "Rasanga Ruwanthi" <ruwanthi_...@yahoo.com > <http://uk.mc1120.mail.yahoo.com/mc/compose?to=ruwanthi_...@yahoo.com>> > > Cc: r-help@r-project.org > <http://uk.mc1120.mail.yahoo.com/mc/compose?to=r-help@r-project.org> > > Date: Thursday, 28 April, 2011, 13:11 > > > > ... and additionally, 'ROC' from the Epi package does the > second step > > all in one. > > > > Am 28.04.2011 13:01, schrieb Eik Vettorazzi: > > > Hi Rasanga, > > > you may have a look at the 'improveProb' function from the Hmisc > > > package. There you can compare the increase in prognostic > power for > > > several combinations of markers. You can create a ROC curve > for a > > > combination of markers by using the predicted risks eg. from a > > logistic > > > regression model. > > > > > > To compare ROC curves of competing markers you can use > 'roc.area.test' > > > from the 'clinfun' package or 'hanley' from gcl. > > > > > > hth. > > > > > > > > > Am 27.04.2011 23:17, schrieb Rasanga Ruwanthi: > > >> Dear list > > >> > > >> I have 5 markers that can be used to detect an infection in > > combination. Could you please advise me how to use functions in > > ROCR/ other package to produce the ROC curve for a combination of > > markers? > > >> > > >> I have used the following to get ROC statistics for each > marker. > > >> pred <- prediction(y$marker1, y$infectn) > > >> perf <-performance(pred,"tpr","fpr") > > >> plot(perf,ave="threshold",spread.estimate="boxplot") > > >> > > >> But I want know whether we could get this for more than one > > marker, so we can look at how good the markers in combination to > > predict the infection. I'm very grateful for any suggestion/help. > > >> > > >> Thanks > > >> Rasanga > > >> > > >> > > >> [[alternative HTML version deleted]] > > >> > > >> > > >> > > >> > > >> ______________________________________________ > > >> R-help@r-project.org > <http://uk.mc1120.mail.yahoo.com/mc/compose?to=R-help@r-project.org> > > > <http://uk.mc1120.mail.yahoo.com/mc/compose?to=R-help@r-project.org> > > mailing list > > >> https://stat.ethz.ch/mailman/listinfo/r-help > > >> PLEASE do read the posting guide > > http://www.R-project.org/posting-guide.html > <http://www.r-project.org/posting-guide.html> > > <http://www.r-project.org/posting-guide.html> > > >> and provide commented, minimal, self-contained, > reproducible code. > > > > > > > > -- > Eik Vettorazzi > > Department of Medical Biometry and Epidemiology > University Medical Center Hamburg-Eppendorf > > Martinistr. 52 > 20246 Hamburg > > T ++49/40/7410-58243 > F ++49/40/7410-57790 > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
