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Run Grid Search Over Simulation Parameters

Source: R/run_grid_simulation.R
run_grid_simulation.Rd

Execute bootstrap simulations across a grid of parameter combinations. Useful for studying how different settings affect edge recovery performance.

Usage

run_grid_simulation(
  Model,
  stable_transitions,
  num_runs,
  n_sequences_vec = NULL,
  seq_length_vec = NULL,
  na_range_list = list(list(min = 0, max = 0)),
  stability_prob = 0.95,
  unstable_mode = "random_jump",
  unstable_random_transition_prob = 0.5,
  unstable_perturb_noise = 0.5,
  unlikely_prob_threshold = 0.1,
  include_na = TRUE,
  consistency_range = c(0.75, 1.25),
  level = 0.05,
  num_cores = parallel::detectCores() - 1,
  num_rows_vec = NULL,
  max_seq_length_vec = 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.

num_runs

Integer. Number of bootstrap runs per parameter combination.

n_sequences_vec

Numeric vector. Values of n_sequences to test.

seq_length_vec

Numeric vector. Values of seq_length to test.

na_range_list

List of lists. Each inner list has min and max elements defining an NA range to test.

stability_prob

Numeric. Fixed stability probability. Default: 0.95.

unstable_mode

Character. Fixed unstable mode. Default: "random_jump".

unstable_random_transition_prob

Numeric. Fixed unstable probability. Default: 0.5.

unstable_perturb_noise

Numeric. Fixed perturbation noise. Default: 0.5.

unlikely_prob_threshold

Numeric. Fixed unlikely threshold. Default: 0.1.

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. Significance level. Default: 0.05.

num_cores

Integer. Number of cores for parallel processing. Default: detectCores() - 1.

num_rows_vec

Deprecated. Use n_sequences_vec instead.

max_seq_length_vec

Deprecated. Use seq_length_vec instead.

Value

A named list where each element corresponds to a parameter combination. Names follow the pattern nr<n_sequences>_sl<seq_length>_na<min>-<max>. Each element contains:

aggregated_summary

Aggregated performance and edge significance.

individual_runs

Per-run details.

successful_runs

Number of successful runs.

parameters

The parameter values used for this combination.

Details

The function creates a full factorial grid from:

  • n_sequences_vec x seq_length_vec x na_range_list

For each combination, it runs run_bootstrap_simulation() and stores the results along with the parameter values used.

Progress messages are printed to track execution.

Examples

if (FALSE) { # \dontrun{
# Create a model
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")

Model <- list(
  weights = trans_mat,
  inits = c(A = 0.33, B = 0.34, C = 0.33)
)

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

# Run grid search
grid_results <- run_grid_simulation(
  Model = Model,
  stable_transitions = stable,
  num_runs = 20,
  n_sequences_vec = c(50, 100, 200),
  seq_length_vec = c(20, 30, 50),
  na_range_list = list(
    list(min = 0, max = 0),
    list(min = 0, max = 5),
    list(min = 5, max = 10)
  ),
  num_cores = 4
)

# Analyze results
summarize_grid_results(grid_results)

# Old parameter names still work
grid_results <- run_grid_simulation(
  Model = Model,
  stable_transitions = stable,
  num_runs = 20,
  num_rows_vec = c(50, 100, 200),
  max_seq_length_vec = c(20, 30, 50)
)
} # }