Compute exact area under the ROC for empirical data.
logical vector, or numeric vector of 0s and 1s, indicating whether each case is a true positive.
numeric vector containing test statistics used to rank cases, from largest to smallest.
A receiver operating curve (ROC) is a plot of sensitivity (true positive rate) versus 1-specificity (false positive rate) for a statistical test or binary classifier. The area under the ROC is a well accepted measure of test performance. It is equivalent to the probability that a randomly chosen pair of cases is corrected ranked.
Here we consider a test statistic
stat, with larger values being more significant, and a vector
truth indicating whether the alternative hypothesis is in fact true.
truth==1 indicates a true discovery and
truth=0 indicates a false discovery.
Correct ranking here means that
truth[i] is greater than or equal to
stat[i] is greater than
The function computes the exact area under the empirical ROC curve defined by
truth when ordered by
stat contains ties, then
auROC returns the average area under the ROC for all possible orderings of
truth for tied
The area under the curve is undefined if
truth is all
TRUE or all
FALSE or if
stat contain missing values.
Numeric value between 0 and 1 giving area under the curve, 1 being perfect and 0 being the minimum.
1 2 3 4
 1  0.5900621
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