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 <h...@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
> >
> >
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