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Summarize Multiple Networks

Source: R/summary_functions.R
summarize_networks.Rd

Compute aggregate statistics across multiple TNA network models.

Usage

summarize_networks(
  model_list,
  include = c("density", "centrality", "edges"),
  threshold = 0.01,
  centrality_measures = c("OutStrength", "InStrength")
)

Arguments

model_list

A list of TNA model objects.

include

Character vector. What to include in the summary. Options: "density" (network density), "centrality" (centrality measures), "edges" (edge weight statistics), or "all" for everything. Default: c("density", "centrality", "edges").

threshold

Numeric. Minimum edge weight to consider present for density calculation. Default: 0.01.

centrality_measures

Character vector. Which centrality measures to summarize. Default: c("OutStrength", "InStrength").

Value

A list containing:

  • summary_table: Data frame with per-network statistics.

  • aggregate: Named list of aggregate statistics across all networks.

  • n_networks: Number of networks summarized.

Details

This function is useful for summarizing results from simulation studies where many networks are generated. It provides both per-network and aggregate statistics.

See also

summarize_simulation() for simulation result summaries, batch_fit_models() for fitting multiple models.

Examples

if (FALSE) { # \dontrun{
# Generate and fit multiple networks
datasets <- lapply(1:20, function(i) {
  simulate_sequences(trans_mat, init_probs, 20, 100)
})
models <- batch_fit_models(datasets)

# Summarize networks
network_summary <- summarize_networks(models)
print(network_summary$aggregate)
} # }