I assume that you have an ordered pair (x, y) data, where x = sensitivity, and
y = 1 - specificity. Your `x' values may or may not be equally spaced. Here
is how you could solve your problem. I show this with an example where we can
compute the area-under the curve exactly:
# Area under the curve
#
# Trapezoidal rule
# x values need not be equally spaced
#
trapezoid <- function(x,y) sum(diff(x)*(y[-1]+y[-length(y)]))/2
#
#
# Simpson's rule when `n' is odd
# Composite Simpson and Trapezoidal rules when `n' is even
# x values must be equally spaced
#
simpson <- function(x, y){
n <- length(y)
odd <- n %% 2
if (odd) area <- 1/3*sum( y[1] + 2*sum(y[seq(3,(n-2),by=2)]) +
4*sum(y[seq(2,(n-1),by=2)]) + y[n])
if (!odd) area <- 1/3*sum( y[1] + 2*sum(y[seq(3,(n-3),by=2)]) +
4*sum(y[seq(2,(n-2),by=2)]) + y[n-1]) + 1/2*(y[n-1] + y[n])
dx <- x[2] - x[1]
return(area * dx)
}
#
# An example for AUC calculation
x <- seq(0, 1, length=21)
roc <- function(x, a) x + a * x * (1 - x)
plot(x, roc(x, a=0.5), type="l")
lines(x, roc(x, a=0.8), col=2)
lines(x, roc(x, a=1.2), col=3)
abline(b=1, lty=2)
y <- roc(x, a=1)
trapezoid(x, y) # exact answer is 2/3
simpson(x, y) # exact answer is 2/3
As you can see the Simpson's rule is more accurate, but the difference should
not matter in applications, as long as you have sufficient number of points for
sensitivity and specificity. Also, note that the improved accuracy of
Simpson's rule is more fully realized when there are "odd" number of `x'
values. If the number of points is even, the trapezoidal rule at the end point
degrades the accuracy of Simpson approximation.
Hope this helps,
Ravi.
____________________________________________________________________
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
----- Original Message -----
From: "olivier.abz" <0509...@rgu.ac.uk>
Date: Thursday, October 22, 2009 10:24 am
Subject: [R] How to calculate the area under the curve
To: r-help@r-project.org
> Hi all,
>
> I would like to calculate the area under the ROC curve for my predictive
> model. I have managed to plot points giving me the ROC curve. However,
> I do
> not know how to get the value of the area under.
> Does anybody know of a function that would give the result I want
> using an
> array of specificity and an array of sensitivity as input?
>
> Thanks,
>
> Olivier
> --
> View this message in context:
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help@r-project.org mailing list
>
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
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