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Compare Two TNA Networks

Source: R/network_comparison.R
compare_networks.Rd

Compare two TNA network models using multiple metrics including correlation, RMSE, and edge-level differences.

Usage

compare_networks(
  model1,
  model2,
  metrics = c("correlation", "rmse", "edge_diff"),
  scaling = c("none", "minmax", "zscore"),
  include_self = TRUE,
  threshold = 0.05
)

Arguments

model1

A TNA model object (the reference/original model).

model2

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

metrics

Character vector. Metrics to compute. Options: "correlation" (Pearson correlation), "rmse" (root mean square error), "mae" (mean absolute error), "edge_diff" (proportion with large differences), "cosine" (cosine similarity), or "all" to compute everything. Default: c("correlation", "rmse", "edge_diff").

scaling

Character. How to scale edge weights before comparison. Options: "none" (no scaling), "minmax" (min-max normalization to 0-1), "zscore" (z-score standardization). Default: "none".

include_self

Logical. Whether to include self-loops in comparison. Default: TRUE.

threshold

Numeric. Threshold for considering an edge as "different" in edge_diff metric. Default: 0.05.

Value

A list with class "tna_comparison" containing:

  • metrics: Named list of computed metrics.

  • edge_comparison: Data frame comparing edges from both models.

  • summary: Character summary of the comparison.

Details

This function extracts edge weights from both models and computes various comparison metrics. Edge weights are extracted from the transition matrices stored in the TNA model objects.

See also

compare_centralities() for comparing centrality profiles, compare_edge_recovery() for edge recovery metrics.

Examples

if (FALSE) { # \dontrun{
# Generate original data and fit model
original_data <- simulate_sequences(trans_mat, init_probs, 20, 200)
model_original <- tna::tna(original_data)

# Simulate from fitted model and fit new model
sim_data <- simulate_sequences(
  transition_matrix = model_original$weights,
  initial_probabilities = model_original$initial,
  max_seq_length = 20, num_rows = 200
)
model_sim <- tna::tna(sim_data)

# Compare the models
comparison <- compare_networks(model_original, model_sim)
print(comparison$metrics)
} # }