Bootstrap for Multi-Group Direct LiNGAM

Usage

lingam_multi_group_bootstrap(
  X_list,
  n_sampling,
  prior_knowledge = NULL,
  apply_prior_knowledge_softly = FALSE,
  reg_method = "adaptive_lasso",
  lambda = "BIC",
  init_method = "ols",
  seed = NULL,
  verbose = TRUE,
  parallel = FALSE,
  n_cores = NULL,
  compute_total_effects = TRUE
)

Arguments

X_list: A list of numeric matrices or data frames (length >= 2), one per group. Same requirements as lingam_multi_group().
n_sampling: Number of bootstrap iterations
prior_knowledge: Prior knowledge matrix (NULL allowed). Applied to every group, same as lingam_multi_group().
apply_prior_knowledge_softly: Apply prior knowledge softly (logical)
reg_method: Regression method ("ols", "lasso", "adaptive_lasso", "ridge")
lambda: Lambda selection ("lambda.min", "lambda.1se", "AIC", "BIC", "oracle")
init_method: Method for estimating the initial weights of adaptive LASSO regression ("ols" or "ridge")
seed: Random seed (NULL allowed)
verbose: Whether to display progress (logical)
parallel: Whether to use parallel processing (logical)
n_cores: Number of cores to use (integer, NULL allowed). When NULL, the number of cores is limited to a maximum of 2 for safety. Ignored when parallel = FALSE.
compute_total_effects: Whether to also compute total causal effects for every variable pair on each bootstrap iteration (logical, default TRUE). Set to FALSE to skip it when only edge/order stability is needed.

Value

A MultiGroupBootstrapResult: a named list (one element per group) of BootstrapResult objects (see lingam_direct_bootstrap()), with class "MultiGroupBootstrapResult".

Details

Each element of the returned list is a regular BootstrapResult, so the existing single-group bootstrap functions (get_probabilities(), get_causal_direction_counts(), get_directed_acyclic_graph_counts(), get_total_causal_effects(), get_causal_order_stability(), plot_bootstrap_probabilities(), tidy()) all work by extracting a group with result[[group_name]] or result[[i]], mirroring the upstream Python API (which returns a list of BootstrapResult per group).

Total effects use path products, not regression. Each bootstrap iteration's total-effect matrix is the sum of path-coefficient products over the DAG defined by that iteration's adjacency matrix (matching the upstream Python MultiGroup bootstrap), which is a different method from lingam_direct_bootstrap()'s regression-based estimate_all_total_effects().

On iteration failures: as in lingam_direct_bootstrap(), each iteration is wrapped in tryCatch(); a failing iteration is skipped with a warning, and only if every iteration fails is an error raised.

On reproducibility: same policy as lingam_direct_bootstrap(). During parallel execution, L'Ecuyer parallel random number streams via parallel::clusterSetRNGStream() are used. Results are reproducible given the same seed and same n_cores, but they do not numerically match the results of sequential execution (parallel = FALSE). If you need results that exactly match the sequential version, use parallel = FALSE.

Examples

mg <- generate_multi_group_sample()

bs <- lingam_multi_group_bootstrap(mg$data_list,
  n_sampling = 10L,
  reg_method = "ols",
  seed = 42
)
#> Multi-group bootstrap: 10 iterations, 2 groups, method=ols (sequential)
#>   iteration 1 / 10
#>   iteration 10 / 10
#> Completed in 0.2 seconds.
get_probabilities(bs[[1]])
#>      [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,]  0.0  0.0  0.4    1  0.0  0.0
#> [2,]  1.0  0.0  1.0    1  0.6  0.8
#> [3,]  0.6  0.0  0.0    1  0.0  0.4
#> [4,]  0.0  0.0  0.0    0  0.0  0.0
#> [5,]  1.0  0.4  1.0    1  0.0  0.7
#> [6,]  1.0  0.2  0.6    1  0.3  0.0

# \donttest{
bs_par <- lingam_multi_group_bootstrap(mg$data_list,
  n_sampling = 30L,
  reg_method = "ols",
  seed = 42,
  parallel = TRUE,
  n_cores = 2L
)
#> Multi-group bootstrap: 30 iterations, 2 groups, method=ols (parallel, 2 cores)
#> Completed in 0.6 seconds.
# }