sens

spec


This function computes the area under ROC curve by using the trapezoidal rule.


 AUC 

This package computes traditional ROC curves and time-dependent ROC curves using the cross-validation, the 0.632 and the 0.632+ estimators. The 0.632+ estimator of time-dependent ROC curve is useful to estimate the predictive accuracy of prognostic signature based on high-dimensional data. For instance, it outperforms the other approaches, especially the cross-validation solution which is often used. The 0.632+ estimators correct the area under the curve in order to adequately estimate the prognostic capacities regardless of the overfitting level. This package also allows for the construction of diagnostic or prognostic scoring systems (penalized regressions). The methodology is adapted to complete data (penalized logistic regression associated with ROC curve) or incomplete time-to-event data (penalized Cox model associated with time-dependent ROC curve).

Y. Foucher

ROC632

Construction of diagnostic or prognostic scoring system and
internal validation of its discriminative capacities based on
ROC curve and 0.633+ boostrap resampling.

AUC: Area under ROC curve from sensitivities and specificities

Description

Usage

Arguments

Details

Examples