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ROC curve and AUC from continuous scores

roc_auc.Rd

Computes the receiver operating characteristic (ROC) curve and area under the curve (AUC) by comparing a continuous score matrix (e.g., PIP) against a binary ground truth adjacency matrix. Only the upper triangle is used.

Usage

roc_auc(score_mat, truth_mat)

Arguments

score_mat

Numeric matrix [p, p]; continuous edge scores (e.g., posterior inclusion probabilities from pip_edges).

truth_mat

Numeric matrix [p, p]; binary ground truth adjacency (1 = edge present, 0 = absent).

Value

A list with three components:

FPR

Numeric vector; false positive rates at each threshold (starts at 0, ends at 1).

TPR

Numeric vector; true positive rates at each threshold (starts at 0, ends at 1).

auc

Numeric scalar; area under the ROC curve. Values near 1 indicate excellent discrimination.

See also

plot_roc(), confusion_at_threshold(), pip_edges()

Examples

sim <- simulate_multiggm(K = 2, p = 8, n = 80, seed = 1)
fit <- multiggm_mcmc(data_list = sim$data_list, burnin = 200, nsave = 100)
pip <- pip_edges(fit)
roc <- roc_auc(pip[,,1], sim$adj_list[[1]])
cat("AUC:", roc$auc, "\n")
#> AUC: 0.8564103