ROC curve

compute a ROC curve

Description

ROC - Receiver Operating Characteristics.
The ROC graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making.
The function computes and plots the classical ROC curve and the mirrored ROC curve (in my opinion, it is more useful).

If you have downloaded partest
(http://www.mathworks.com/matlabcentral/fileexchange/12705) the routine will compute several data on test performance.

You can find more informations on:
http://www.advancedmcode.org/roc-curve.html

• MATLAB release: MATLAB 7.6 (R2008a)
• Other requirements: Optional: partest.m http://www.mathworks.com/matlabcentral/fileexchange/12705
• Other Files: license.txt, ; roc.m, ; rocdata.mat

[Brahim HAMADICHAREF]

Good stuff.

[Truc Phan]

 

[Diego García Bascuñán]

Good function

[Günther Eibl]

easy to use!

[Pawel]

 

[Phong Vo]

Thank you very much!

[cabrego]

Nice function, but I think it may have a bug, I am getting different results for significantly overlaping distributions when I compare to medcalc, and online calculators.

email me for more details: [e-mail]

[Michael]

Agree with cabrego, this algorithm does not work correctly. Depending on the input data, it generates ROC curves with specificity and sensitivity backward. I believe this is because elements that fall below a cutoff value (I in the code) are called "true positives" when they should be "false positives". The convention is that higher values of a test are abnormal (positive).

I confirmed that other software (online ROC calculator, ROCR in R, STATA) does not behave this way with the same input data and all others produce correct results.

Use at your own risk.

[cabrego]

I tested the new release and it is agreeing with other codes now. Michael, you may also wish to verify that the new version is working correctly.

I also think adding the cut off points as an additional x or y axis would be very useful to understand the trade off between sensitivity and sensibility.

[Jens Kaftan]

Hi Giuseppe.
I have had a look at the new release today and I think it is still not perfectly correct. I have validated the scripts using the example data of Hanley and McNeil's 1982 paper: "The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve", which seems to be the basis for the calculations (such as the approximation of Q_1 and Q_2) anyways. To my opinion the problem is that when integrating over the ROC curve to compute the AUC, the data point (sensitivity=1, specificity=0) is not considered when using the trapezoidal rule. Consequently the AUC value (and all AUC dependent measures) differ slightly from the example in the mentioned article (which becomes more severe for non-continous tests with only a few cut-off points).

Best,
Jens.