Bootstrap for RCD
Usage
lingam_rcd_bootstrap(
X,
n_sampling,
max_explanatory_num = 2L,
cor_alpha = 0.01,
ind_alpha = 0.01,
shapiro_alpha = 0.01,
MLHSICR = FALSE,
independence = "hsic",
ind_corr = 0.5,
seed = NULL,
verbose = TRUE,
parallel = FALSE,
n_cores = NULL,
compute_total_effects = TRUE
)Arguments
- X
Numeric matrix (n_samples x n_features)
- n_sampling
Number of bootstrap iterations
- max_explanatory_num
Maximum number of explanatory variables, passed to
lingam_rcd()- cor_alpha
Significance level for correlation tests, passed to
lingam_rcd()- ind_alpha
Significance level for the HSIC independence test, passed to
lingam_rcd()- shapiro_alpha
Significance level for the non-Gaussianity test, passed to
lingam_rcd()- MLHSICR
Whether to use MLHSICR regression, passed to
lingam_rcd()- independence
Independence measure, passed to
lingam_rcd()- ind_corr
F-correlation rejection threshold, passed to
lingam_rcd()- 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)
- compute_total_effects
Whether to also estimate total causal effects for every ancestor pair on each bootstrap iteration (logical, default
TRUE).
Value
A BootstrapResult (list); see lingam_direct_bootstrap() for the
query helpers that operate on it (get_probabilities(),
get_causal_direction_counts(), get_directed_acyclic_graph_counts(),
get_total_causal_effects()).
Details
Total effects are computed only for ancestor pairs, driven by each
iteration's ancestors_list (unlike lingam_direct_bootstrap() and
lingam_parce_bootstrap(), which loop over all variable pairs). For a
pair (from, to) with from in to's ancestor set, the total effect is
obtained via calculate_total_effect() (summing products of
adjacency-matrix coefficients along every directed path). If from's row
in the adjacency matrix contains NA (it shares a latent confounder with
some other variable), the effect is set to NA for that iteration.
NA (confounded) edges are treated as absent when aggregating. Both
the adjacency matrix and the total-effect matrix have NA replaced by
0 before being stored in the returned BootstrapResult, matching the
policy used by lingam_parce_bootstrap().
causal_orders is not populated: RCD has no causal order (see
lingam_rcd()). As a result, get_causal_order_stability() cannot be
used with a BootstrapResult returned by this function.
RCD's fit step is HSIC-heavy and can be slow per iteration, especially
with MLHSICR = TRUE; keep n_sampling modest in examples.
Examples
# \donttest{
confounded <- generate_rcd_sample(n = 300, seed = 1)
bs <- lingam_rcd_bootstrap(confounded$data,
n_sampling = 5L,
seed = 42
)
#> Bootstrap: 5 iterations, RCD (sequential)
#> iteration 1 / 5
#> Completed in 4.3 seconds.
get_probabilities(bs)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.0 0.4 0 0.0 0 0.4
#> [2,] 0.0 0.0 0 0.0 0 0.2
#> [3,] 0.2 0.0 0 0.4 0 0.2
#> [4,] 0.0 0.0 0 0.0 0 0.0
#> [5,] 1.0 0.0 0 0.0 0 0.0
#> [6,] 0.0 0.0 0 0.0 0 0.0
# }