Acceptance and admissibility diagnostics for bsvarSIGNs — acceptance

Summarise stored-draw diagnostics for sign-restricted posterior objects. These diagnostics do not reconstruct the full proposal/rejection history of the sampler. Instead, they report what can be recovered from the saved posterior state, identification pattern, and admissibility weights.

Usage

acceptance_diagnostics(object, ...)

# S3 method for class 'PosteriorBSVARSIGN'
acceptance_diagnostics(
  object,
  kernel_tol = 1e-12,
  ess_threshold = 20,
  sparse_threshold = 0.1,
  model = "model1",
  ...
)

# Default S3 method
acceptance_diagnostics(object, ...)

Arguments

object: A PosteriorBSVARSIGN object.
...: Reserved for future extensions.
kernel_tol: Numerical tolerance used to classify near-zero admissibility weights.
ess_threshold: Effective-sample-size threshold below which a warning flag is raised.
sparse_threshold: Share of near-zero admissibility weights above which a sparse-support warning flag is raised.
model: Optional model identifier.

Value

A bsvar_post_tbl with columns model, object_type, metric, value, flag, and message. Each row reports one diagnostic metric.

Examples

# \donttest{
data(optimism, package = "bsvarSIGNs")
sign_irf <- matrix(c(1, rep(NA, 3)), 2, 2)
spec_s <- suppressMessages(
  bsvarSIGNs::specify_bsvarSIGN$new(optimism[, 1:2], p = 1,
                                     sign_irf = sign_irf)
)
post_s <- bsvars::estimate(spec_s, S = 5, show_progress = FALSE)

diag <- acceptance_diagnostics(post_s)
print(diag)
#> # A tibble: 13 × 6
#>    model  object_type            metric                      value flag  message
#>    <chr>  <chr>                  <chr>                       <dbl> <lgl> <chr>  
#>  1 model1 acceptance_diagnostics posterior_draws                 5 FALSE ""     
#>  2 model1 acceptance_diagnostics effective_sample_size           5 TRUE  "ESS b…
#>  3 model1 acceptance_diagnostics max_tries                     Inf FALSE ""     
#>  4 model1 acceptance_diagnostics irf_sign_restrictions           1 FALSE ""     
#>  5 model1 acceptance_diagnostics zero_restrictions               0 FALSE ""     
#>  6 model1 acceptance_diagnostics structural_sign_restrictio…     0 FALSE ""     
#>  7 model1 acceptance_diagnostics narrative_restrictions          0 FALSE ""     
#>  8 model1 acceptance_diagnostics kernel_mean                     1 FALSE ""     
#>  9 model1 acceptance_diagnostics kernel_median                   1 FALSE ""     
#> 10 model1 acceptance_diagnostics kernel_min                      1 FALSE ""     
#> 11 model1 acceptance_diagnostics kernel_max                      1 FALSE ""     
#> 12 model1 acceptance_diagnostics kernel_zero_share               0 FALSE ""     
#> 13 model1 acceptance_diagnostics kernel_cv                       0 FALSE ""     
# }