Bootstrap probabilities for a VARMA-LiNGAM model — get_varma

Returns, for each entry of the joined matrix, the fraction of bootstrap samples in which that edge exceeded min_causal_effect.

Usage

get_varma_probabilities(result, min_causal_effect = NULL)

Arguments

result: a VARMABootstrapResult object
min_causal_effect: minimum |effect| threshold (NULL = 0)

Value

probability matrix (n_features x n_features*(1 + p + q)). Columns 1..n_features are the instantaneous block, the next p blocks are the AR lags 1..p (psi), and the final q blocks are the MA terms 1..q (omega). P[i, j] is the probability of the edge j -> i.

Examples

s <- generate_varmalingam_sample(n = 300, seed = 42)
bs <- lingam_varma_bootstrap(s$data,
  n_sampling = 5L, order = c(1, 1), criterion = NULL,
  reg_method = "ols", prune = FALSE, seed = 1, verbose = FALSE
)
get_varma_probabilities(bs)
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#> [1,]    0    0    0    1    1    1    1    1    1
#> [2,]    1    0    0    1    1    1    1    1    1
#> [3,]    1    1    0    1    1    1    1    1    1