movieROC

Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC curve); - transforming the marker by a function either defined by the user or resulting from a logistic regression model (hROC curve); - considering a linear transformation with some fixed parameters introduced by the user, dynamic parameters or empirically maximizing TPR for each FPR for a bivariate marker. Also a quadratic transformation with particular coefficients or a function fitted by a logistic regression model can be considered (biROC curve); - considering a linear transformation with some fixed parameters introduced by the user, dynamic parameters or a function fitted by a logistic regression model (multiROC curve). The classification regions behind each point of the ROC curve are displayed in both fixed graphics (plot.buildROC(), plot.regions() or plot.funregions() function) or videos (movieROC() function).