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Enumerate bootstrap paths between two variables in a VAR-LiNGAM model

Source: R/lingam_var_bootstrap.r
get_var_paths.Rd

Builds the time-expanded graph for every bootstrap sample and enumerates all directed paths from the source (at from_lag) to the destination (at to_lag), reporting each path's bootstrap probability and median effect. Port of the Python reference VARBootstrapResult.get_paths.

Usage

get_var_paths(
  result,
  from_index,
  to_index,
  from_lag = 0,
  to_lag = 0,
  min_causal_effect = NULL
)

Arguments

result

a VARBootstrapResult object

from_index

source variable (1-based)

to_index

destination variable (1-based)

from_lag

lag of the source (default 0)

to_lag

lag of the destination (default 0); must satisfy to_lag <= from_lag

min_causal_effect

minimum |effect| threshold (NULL = 0)

Value

a data frame (path, effect, probability), one row per distinct path

Details

Node indices in the returned path are 1-based positions in the time-expanded graph: column j of block L (lag L) corresponds to index n_features * L + j.

Examples

s <- generate_varlingam_sample(n = 500, seed = 42)
bs <- lingam_var_bootstrap(s$data,
  n_sampling = 10L, criterion = NULL,
  reg_method = "ols", prune = FALSE, seed = 1, verbose = FALSE
)
get_var_paths(bs, from_index = 1, to_index = 3)
#>      path        effect probability
#> 1 1, 2, 3 -0.3077611427           1
#> 2    1, 3 -0.0007678152           1