What is the significance level of the ROC curve?
The 95% Confidence Interval is the interval in which the true (population) Area under the ROC curve lies with 95% confidence. The Significance level or P-value is the probability that the observed sample Area under the ROC curve is found when in fact,…
Which is smaller the AUC of ROC1 or roc2?
If alternative=”less”, the alternative is that the AUC of roc1 is smaller than the AUC of roc2. For method=”venkatraman”, only “two.sided” test is available.
How to compare two ROC curves in Proc?
For method=”venkatraman”, only “two.sided” test is available. If the paired argument is not provided, the are.paired function is employed to detect the paired status of the ROC curves. It will test if the original response is identical between the two ROC curves (this is always the case if the call is made with roc.test.default ).
Can you use Venkatraman ROC test in Proc?
Using “delong” for partial AUC and smoothed ROCs is not supported in pROC and result in an error. It is spurious to use “delong” for roc with different direction (a warning is issued but the spurious comparison is enforced). “venkatraman”’s test cannot be employed to compare smoothed ROC curves, or curves with partial AUC specifications.
Is the ROC curve always a perfect recall?
The ROC curve on the left does a pretty good job at classifying the 1s, or True instances of our data, as it almost always presents a perfect recall (this means it hugs the upper limit of our chart). However, when it reaches the point marked in blue, it considerably drops in recall while transitioning to a perfect specificity.
Which is the correct ROC curve for perfect discrimination?
A test with perfect discrimination (no overlap in the two distributions) has a ROC curve that passes through the upper left corner (100% sensitivity, 100% specificity). Therefore the closer the ROC curve is to the upper left corner, the higher the overall accuracy of the test (Zweig & Campbell, 1993).