Package 'limma' - Bioconductor
Package 'limma' - Bioconductor
Package 'limma' - Bioconductor
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24 auROC<br />
auROC<br />
Area Under Receiver Operating Curve<br />
Description<br />
Compute exact area under the ROC for empirical data.<br />
Usage<br />
auROC(truth, stat=NULL)<br />
Arguments<br />
truth<br />
stat<br />
logical vector, or numeric vector of 0s and 1s, indicating whether each case is a<br />
true positive.<br />
numeric vector containing test statistics used to rank cases, from largest to smallest.<br />
If NULL, then truth is assumed to be already sorted in decreasing test statistic<br />
order.<br />
Details<br />
A receiver operating curve (ROC) is a plot of sensitivity (true positive rate) versus error (false<br />
positive rate) for a statistical test or binary classifier. The area under the ROC is a well accepted<br />
measure of test performance. It is equivalent to the probability that a randomly chosen pair of cases<br />
is corrected ranked.<br />
Here we consider a test statistic stat, with larger values being more significant, and a vector truth<br />
indicating whether the null hypothesis is in fact true. Correct ranking here means that truth[i] is<br />
greater than or equal to truth[j] when stat[i] is greater than stat[j]. The function computes<br />
the exact area under the empirical ROC curve defined by truth when ordered by stat.<br />
Value<br />
Numeric vector giving area under the curve, 1 being perfect and 0 being the minimum, or NULL if<br />
truth has zero length.<br />
Author(s)<br />
Gordon Smyth<br />
See Also<br />
See 08.Tests for other functions for testing and processing p-values.
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