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Calculate Edge Recovery Metrics

Source: R/network_comparison.R
compare_edge_recovery.Rd

Calculate metrics for how well edges from an original network are recovered in a simulated/comparison network.

Usage

compare_edge_recovery(
  original,
  simulated,
  threshold = 0.01,
  return_edges = FALSE
)

calculate_edge_recovery(...)

Arguments

original

A TNA model object (the original/ground truth model).

simulated

A TNA model object (the simulated/recovered model).

threshold

Numeric. Minimum edge weight to consider an edge as "present". Default: 0.01.

return_edges

Logical. Whether to return detailed edge-level results. Default: FALSE.

...

Arguments passed to compare_edge_recovery.

Value

A list containing:

  • true_positives: Number of edges correctly present in both.

  • false_positives: Number of edges present in simulated but not original.

  • false_negatives: Number of edges present in original but not simulated.

  • true_negatives: Number of edges correctly absent in both.

  • precision: TP / (TP + FP).

  • recall: TP / (TP + FN), also known as sensitivity.

  • f1_score: Harmonic mean of precision and recall.

  • accuracy: (TP + TN) / total edges.

  • jaccard: TP / (TP + FP + FN), Jaccard similarity.

  • edges: (Optional) Data frame with edge-level results.

Details

This function treats edge recovery as a binary classification problem:

  • True Positive: Edge present in both original and simulated.

  • False Positive: Edge present in simulated but not original.

  • False Negative: Edge present in original but not simulated.

  • True Negative: Edge absent in both.

An edge is considered "present" if its weight exceeds the threshold.

See also

compare_networks() for full network comparison, run_bootstrap_iteration() for bootstrap evaluation.

Examples

if (FALSE) { # \dontrun{
# Calculate edge recovery
recovery <- compare_edge_recovery(model_original, model_simulated)
print(sprintf("Precision: %.2f, Recall: %.2f", recovery$precision, recovery$recall))
} # }