R help - Jan 2011 - auc function

Hi, there.

Suppose I already have sensitivities and specificities. What is the quick
R-function to calculate AUC for the ROC plot? There seem to be many R functions
to calculate AUC.

Thanks.

Yulei


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ROCR



On Thu, Jan 20, 2011 at 3:04 PM, He, Yulei <he@hcp.med.harvard.edu> wrote:
> Hi, there.
>
> Suppose I already have sensitivities and specificities. What is the quick
> R-function to calculate AUC for the ROC plot? There seem to be many R
> functions to calculate AUC.
>
> Thanks.
>
> Yulei
>
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide
>
http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html>
> and provide commented, minimal, self-contained, reproducible code.
>


-- 
Sincerely,
Changbin
--

Changbin Du
DOE Joint Genome Institute
Bldg 400 Rm 457
2800 Mitchell Dr
Walnut Creet, CA 94598
Phone: 925-927-2856

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On Thu, Jan 20, 2011 at 03:14:01PM -0800, Changbin Du
wrote:
> ROCR
> 
I appreciate this information, which is new for me. Up to now, i was
using the function

  get.auc <- function(statistic, label, negative, positive)
  {
      xmove <- as.numeric(label == negative)
      ymove <- as.numeric(label == positive)
      stopifnot(xmove + ymove == 1)
      rank.stat <- rank(statistic, ties.method="min")
      steps <- aggregate(cbind(xmove, ymove), by=list(rank.stat), sum)
      n <- nrow(steps)
      x <- c(0, cumsum(steps[n:1, 2]))
      y <- c(0, cumsum(steps[n:1, 3]))
      sum(diff(x) * (y[1:n] + y[2:(n+1)]))/(2*max(x)*max(y))
  }

CRAN package ROCR allows to compute many different measures and
visualisations of classifier performance. In particular, AUC may
be computed as follows

  library(ROCR)

  n <- 50
  label <- ordered(rep(c("c1", "c2"), length=n))
  set.seed(12345)
  statistic <- rnorm(n) + (label == "c2")
  pred <- prediction(statistic, label)
  AUC <- performance(pred, "auc")@y.values[[1]]
  cbind(AUC, diff=AUC - get.auc(statistic, label, "c1",
"c2"))
  #         AUC diff
  # [1,] 0.7392    0

The difference is not always exactly zero, but is at the level
of the machine rounding error.

Petr Savicky.
> 
> 
> On Thu, Jan 20, 2011 at 3:04 PM, He, Yulei <he at
hcp.med.harvard.edu> wrote:
> 
> > Hi, there.
> >
> > Suppose I already have sensitivities and specificities. What is the
quick
> > R-function to calculate AUC for the ROC plot? There seem to be many R
> > functions to calculate AUC.
> >
> > Thanks.
> >
> > Yulei
> >
> >