Compute ROC curve and AUC

Computes the ROC curve (TPR vs FPR at varying thresholds) and the area under the curve (AUC) for edge selection. Only the upper triangle of the matrices is used.

Usage

roc_auc(score_mat, truth_mat)

Arguments

score_mat

Numeric matrix of continuous scores (e.g., edge inclusion probabilities or absolute partial correlations).

truth_mat

Binary matrix of true edges (1 = edge, 0 = no edge).

Value

A list with elements:

FPR

Numeric vector of false positive rates.

TPR

Numeric vector of true positive rates.

thresholds

Numeric vector of thresholds used.

AUC

Scalar area under the ROC curve (trapezoidal rule).

Examples

truth <- matrix(c(0,1,0,0, 1,0,1,0, 0,1,0,1, 0,0,1,0), 4, 4)
scores <- matrix(c(0,.9,.1,.05, .9,0,.8,.1, .1,.8,0,.7, .05,.1,.7,0), 4, 4)
roc <- roc_auc(scores, truth)
roc$AUC
#> [1] 1