ROC Hard? No, ROC Made Easy! – Award Winning SAS Student Ambassador Paper



A ROC (Receiver Operator Characteristic) curve shows how well two groups are separated by plotting the Sensitivity by 1 – Specificity. When used alone, the cut off used to balance the desired Sensitivity and Specificity is not shown. Furthermore the ROC curve does not display the interesting counts and percentages of the True Positives, True Negatives, False Positives, and False Negatives.

The macro presented in this paper uses Graph Template Language (GTL) and the Statistical Graphics Engine (SGE) to quickly and easily create an informative graph that can be edited and which displays the Sensitivity and Specificity of your binary classifier. The macro also displays scatter and box plots of the desired variable by the binary classifiers to identify unusual results. Options also display a confusion matrix and area under the curve (AUC) results. The confusion matrix is calculated from the default Sensitivity and Specificity intersection, but the cut off can be made explicitly. The AUC statistic shows how well the binary classifier discriminates. This macro is used with SAS® 9.2 Phase 2.