Run Bootstrap Simulation Across Multiple Runs — run_bootstrap

Simulate and analyze edge recovery across multiple bootstrap runs. Aggregates results to compute overall performance metrics and edge-level significance.

Usage

run_bootstrap_simulation(
  Model,
  stable_transitions,
  num_runs,
  seq_length = 20,
  n_sequences = 100,
  stability_prob = 0.95,
  unstable_mode = "random_jump",
  unstable_random_transition_prob = 0.5,
  unstable_perturb_noise = 0.5,
  unlikely_prob_threshold = 0.1,
  na_range = c(0, 0),
  include_na = TRUE,
  consistency_range = c(0.75, 1.25),
  level = 0.05,
  num_cores = parallel::detectCores() - 1,
  max_seq_length = NULL,
  num_rows = NULL,
  min_na = NULL,
  max_na = NULL
)

Arguments

Model: A TNA model object with weights (transition matrix) and inits (initial probabilities).
stable_transitions: List of character vectors defining ground truth stable transitions. Each vector contains two state names (from, to).
num_runs: Integer. Number of simulation runs to perform.
seq_length: Integer. Maximum sequence length.
n_sequences: Integer. Number of sequences per run.
stability_prob: Numeric in (0 to 1). Probability of following stable transitions. Default: 0.95.
unstable_mode: Character. Mode for unstable transitions: "random_jump", "perturb_prob", or "unlikely_jump". Default: "random_jump".
unstable_random_transition_prob: Numeric in (0 to 1). Probability of unstable action. Default: 0.5.
unstable_perturb_noise: Numeric in (0 to 1). Noise for perturbation mode. Default: 0.5.
unlikely_prob_threshold: Numeric in (0 to 1). Threshold for unlikely transitions. Default: 0.1.
na_range: Integer vector of length 2 (min, max) or single integer. Range of NA values per sequence. Default: c(0, 0).
include_na: Logical. Whether to include NAs. Default: TRUE.
consistency_range: Numeric vector of length 2. Bootstrap consistency range. Default: c(0.75, 1.25).
level: Numeric in (0,1). Significance level. Default: 0.05.
num_cores: Integer. Number of cores for parallel processing. Default: detectCores() - 1.
max_seq_length: Deprecated. Use seq_length instead.
num_rows: Deprecated. Use n_sequences instead.
min_na: Deprecated. Use na_range instead.
max_na: Deprecated. Use na_range instead.

Value

A list containing:

aggregated_summary

List with:

overall_performance: Data frame with Sensitivity, Specificity, FPR, FNR.
edge_significance: Data frame with per-edge recovery rates and statistics.

individual_runs

List with:

list_of_summaries: Bootstrap summaries from each run.
list_of_per_edge_performance: Per-edge results from each run.

successful_runs

Number of successfully completed runs.

Details

For each run, the function calls run_bootstrap_iteration() and accumulates TP/TN/FP/FN counts. After all runs, it computes:

Overall Performance: Aggregated sensitivity (TPR), specificity (TNR), false positive rate (FPR), and false negative rate (FNR).
Edge Significance: For each edge, the number of times it was detected as significant, average p-value, average weight, and recovery rate.

Parallel processing uses parallel::mclapply().

Examples

if (FALSE) { # \dontrun{
# First create a TNA model from some data
trans_mat <- matrix(c(
  0.6, 0.3, 0.1,
  0.2, 0.6, 0.2,
  0.1, 0.2, 0.7
), nrow = 3, byrow = TRUE)
rownames(trans_mat) <- colnames(trans_mat) <- c("A", "B", "C")

# Create a mock Model object
Model <- list(
  weights = trans_mat,
  inits = c(A = 0.33, B = 0.34, C = 0.33)
)

# Define stable transitions
stable <- list(c("A", "B"), c("B", "C"))

# Run bootstrap simulation
results <- run_bootstrap_simulation(
  Model = Model,
  stable_transitions = stable,
  num_runs = 50,
  seq_length = 30,
  n_sequences = 100,
  stability_prob = 0.95,
  unstable_mode = "random_jump",
  num_cores = 4
)

# View overall performance
results$aggregated_summary$overall_performance

# View edge-level results
results$aggregated_summary$edge_significance

# Old parameter names still work
results <- run_bootstrap_simulation(
  Model = Model,
  stable_transitions = stable,
  num_runs = 50,
  max_seq_length = 30,
  num_rows = 100
)
} # }