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.Vettorazzi at uke.uni-hamburg.de>/* wrote:
>
>
> From: Eik Vettorazzi <E.Vettorazzi at uke.uni-hamburg.de>
> Subject: Re: [R] ROCR for combination of markers
> To: "Rasanga Ruwanthi" <ruwanthi_kdr at yahoo.com>
> Cc: "r-help" <r-help at 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.Vettorazzi at uke.uni-hamburg.de
> <http://uk.mc1120.mail.yahoo.com/mc/compose?to=E.Vettorazzi at
uke.uni-hamburg.de>>/*
> wrote:
> >
> >
> > From: Eik Vettorazzi <E.Vettorazzi at uke.uni-hamburg.de
> <http://uk.mc1120.mail.yahoo.com/mc/compose?to=E.Vettorazzi at
uke.uni-hamburg.de>>
> > Subject: Re: [R] ROCR for combination of markers
> > To: "Rasanga Ruwanthi" <ruwanthi_kdr at yahoo.com
> <http://uk.mc1120.mail.yahoo.com/mc/compose?to=ruwanthi_kdr at
yahoo.com>>
> > Cc: r-help at r-project.org
> <http://uk.mc1120.mail.yahoo.com/mc/compose?to=r-help at
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 at r-project.org
> <http://uk.mc1120.mail.yahoo.com/mc/compose?to=R-help at
r-project.org>
> >
> <http://uk.mc1120.mail.yahoo.com/mc/compose?to=R-help at
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
>