VUScov: Covariance of two volumes under the ROC surface

In VUROCS: Volume under the ROC Surface for Multi-Class ROC Analysis

Description

Computes the covariance of the two volumes under the ROC surface (VUS) implied by two predictions fx1 and fx2 (i.e. values of two ranking functions f1 and f2) for a vector of realisations y (i.e. realised categories) in a multi-class classification problem.

Usage

VUScov(y, fx1, fx2, ncores = 1, clusterType = "SOCK")

Arguments

y	a vector of realized categories.
fx1	a vector of predicted values of the ranking function f1.
fx2	a vector of predicted values of the ranking function f2.
ncores	number of cores to be used for parallelized computations. Its default value is 1.
clusterType	type of cluster to be initialized in case more than one core is used for calculations. Its default value is "SOCK". For details regarding the different types to be used, see makeCluster.

Value

The implemented algorithm is based on Waegeman, De Baets and Boullart (2008). A list of length three is returned, containing the following components:

cov	covariance of the two volumes under the ROC surface implied by f1 and f2
val_f1	volume under the ROC surface implied by f1
val_f2	volume under the ROC surface implied by f2

References

Waegeman W., De Baets B., Boullart L., 2008. On the scalability of ordered multi-class ROC analysis. Computational Statistics & Data Analysis 52, 3371-3388.

Examples

VUScov(c(1,2,1,3,2,3),c(1,2,3,4,5,6),c(1,3,2,4,6,5))

VUROCS documentation built on April 14, 2020, 6:47 p.m.