Compute comprehensive evaluation metrics for an ssjgl fit

Given an ssjgl fit object and the true adjacency/precision matrices, computes edge-level metrics (TPR, FPR, AUC) and estimation error (Frobenius norm, KL divergence).

Usage

compute_metrics(
  fit,
  true_adj,
  true_omega = NULL,
  v0_index = NULL,
  threshold = 0.5
)

Arguments

fit: An object of class ssjgl.
true_adj: List of K binary adjacency matrices (true graph structure).
true_omega: List of K true precision matrices. Default NULL.
v0_index: Integer index into the v0 ladder. Default NULL uses the last step.
threshold: Threshold for binarizing edge probabilities. Default 0.5.

Value

A list with elements:

per_group: List of K sublists, each with TP, FP, TN, FN, TPR, FPR, precision, F1, frobenius_norm, and KL_divergence.
roc: List of K ROC objects (from roc_auc).
overall: Named list with mean TPR, mean FPR, mean AUC across groups.

Examples

sim <- simulate_ssjgl_data(K = 2, p = 10, n = 50, seed = 1)
fit <- ssjgl(sim$data_list, penalty = "fused",
             lambda0 = 1, lambda1 = 0.5, lambda2 = 0.5,
             v0s = 0.01, maxitr.em = 10, impute = FALSE)
#> Ladder= 1 v0 = 0.01 done. Time: 0
m <- compute_metrics(fit, sim$adj_list, sim$Omega_list)
m$overall
#> $mean_TPR
#> [1] 0.9428105
#> 
#> $mean_FPR
#> [1] 0.3452381
#> 
#> $mean_AUC
#> [1] 0.8783112
#>