Bootstrap for VAR-LiNGAM

Evaluates the statistical reliability of the estimated time-series DAG by resampling. Unlike the i.i.d. row resampling used for Direct LiNGAM, this uses a residual bootstrap: the VAR is fitted once on the original data, the residuals are resampled with replacement, and a new series is rebuilt by the VAR recursion before re-estimating VAR-LiNGAM on it (this preserves the autoregressive structure). Port of the Python reference VARLiNGAM.bootstrap.

Usage

lingam_var_bootstrap(
  X,
  n_sampling,
  lags = 1L,
  criterion = "bic",
  measure = "pwling",
  reg_method = "adaptive_lasso",
  lambda = "BIC",
  init_method = "ols",
  prune = TRUE,
  seed = NULL,
  verbose = TRUE,
  parallel = FALSE,
  n_cores = NULL
)

Arguments

X

numeric matrix or data frame (n_samples x n_features), rows ordered in time.

n_sampling

number of bootstrap iterations (positive integer).

lags

maximum lag order. When criterion is not NULL, the lag is selected once on the original data and then fixed across all iterations.

criterion

lag-selection criterion ("bic", "aic", "hqic", "fpe") or NULL to use lags directly.

measure

independence measure for lingam_direct() ("pwling"/"kernel").

reg_method

regression method for the instantaneous matrix.

lambda

penalty selection (see lingam_direct()).

init_method

initial-weight method for adaptive LASSO.

prune

logical; passed to lingam_var() on each iteration (default TRUE).

seed

random seed (NULL allowed).

verbose

whether to print progress (logical).

parallel

whether to distribute iterations across cores (logical).

n_cores

number of cores (integer or NULL; NULL caps at 2 for safety).

Value

a VARBootstrapResult object.

Details

Reproducibility follows the same rules as lingam_direct_bootstrap(): with parallel = TRUE, L'Ecuyer streams via parallel::clusterSetRNGStream() make results reproducible for a given seed and n_cores, but they do not match the sequential (parallel = FALSE) results.

On iteration failures: as in lingam_direct_bootstrap(), each iteration runs inside a tryCatch(); a failing iteration is reported as a warning and excluded from the result instead of aborting the run. An error is raised only if every iteration fails.

Examples

s <- generate_varlingam_sample(n = 500, seed = 42)

# Fast example: OLS instantaneous structure, no pruning (no glmnet needed)
bs <- lingam_var_bootstrap(s$data,
  n_sampling = 10L, lags = 1, criterion = NULL,
  reg_method = "ols", prune = FALSE, seed = 1, verbose = FALSE
)
get_var_probabilities(bs)
#>      [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,]    0    0    0    1    1    1
#> [2,]    1    0    0    1    1    1
#> [3,]    1    1    0    1    1    1