Receiver Operating Characteristics
The ROC graphs are a useful tecnique for organizing classifiers and
visualizing their performance. ROC graphs are commonly used in medical
decision making.
Syntax: ROCout=roc(x,thresholds,alpha,verbose)
Input: x - This is a Nx2 data matrix. The first column is the column of the data value; The second column is the column of the tag: unhealthy (1) and healthy (0). Thresholds - If you want to use all unique values in x(:,1) then set this variable to 0 or leave it empty; else set how many unique values you want to use (min=3); alpha - significance level (default 0.05) verbose - if you want to see all reports and plots (0-no; 1-yes by default);
Output: if verbose = 1 the ROCplots, the sensitivity and specificity at thresholds; the Area under the curve with Standard error and Confidence interval and comment. if ROCout is declared, you will have a struct: ROCout.AUC=Area under the curve (AUC); ROCout.SE=Standard error of the area; ROCout.ci=Confidence interval of the AUC ROCout.co=Cut off points ROCdata.xr and ROCdata.yr points for ROC plot
Created by Giuseppe Cardillo
giuseppe.cardillo-edta@poste.it
To cite this file, this would be an appropriate format: Cardillo G. (2008) ROC curve: compute a Receiver Operating Characteristics curve. http://www.mathworks.com/matlabcentral/fileexchange/19950