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Run Network Analysis Simulations

Source: R/run_network_simulation.R
run_network_simulation.Rd

Run TNA simulations comparing fitted models to original data. Supports multiple model types, comparison modes, and parallel processing.

Usage

run_network_simulation(
  original_data_list,
  sim_params = NULL,
  models = c("tna"),
  comparisons = c("original"),
  num_runs = 3,
  parallel = FALSE,
  scaling = "minmax"
)

Arguments

original_data_list

A list of data frames containing original sequence data, or a single data frame. Used as reference for comparisons.

sim_params

Simulation parameters. Can be:

  • NULL (uses defaults).

  • A list of parameters.

  • A data frame where each row is a parameter combination.

models

Character vector of model types to simulate. Options: "tna", "ftna", "ctna", "atna". Default: c("tna").

comparisons

Character vector of comparison types. Options:

"original"

Compare simulated models to original data.

"across_models"

Compare different model types against each other.

"ftna_reference"

Compare all models to their ftna counterparts.

Default: c("original").

num_runs

Integer. Number of simulation runs per parameter set. Default: 3.

parallel

Logical. Whether to use parallel processing. Default: FALSE.

scaling

Character. Scaling method for comparisons. Options: "none", "minmax", "zscore". Default: "minmax".

Value

A list containing:

metrics

Data frame of all comparison metrics from all runs.

summary_stats

Aggregated summary statistics by model and comparison type.

parameters

List of simulation parameters used.

Details

For each parameter combination and original dataset, the function:

  1. Extracts transition probabilities from the original TNA model.

  2. Generates simulated sequences using those probabilities.

  3. Fits the specified model types to the simulated sequences.

  4. Computes comparison metrics against reference models.

Parallel processing uses the future.apply package when enabled.

Examples

if (FALSE) { # \dontrun{
# Create some original sequence data
trans_mat <- matrix(c(
  0.7, 0.2, 0.1,
  0.3, 0.5, 0.2,
  0.2, 0.3, 0.5
), nrow = 3, byrow = TRUE)
rownames(trans_mat) <- colnames(trans_mat) <- c("A", "B", "C")
init_probs <- c(A = 0.5, B = 0.3, C = 0.2)

original_data <- simulate_sequences(
  trans_matrix = trans_mat,
  init_probs = init_probs,
  seq_length = 30,
  n_sequences = 100
)

# Run simulations comparing tna and ftna models
results <- run_network_simulation(
  original_data_list = original_data,
  sim_params = list(seq_length = 30, n_sequences = 100),
  models = c("tna", "ftna"),
  comparisons = c("original"),
  num_runs = 5
)

# View summary statistics
results$summary_stats
} # }