Compares acceptance diagnostics across model specifications

Computes effective sample size and admissibility-weight diagnostics for two or more sign-restricted posterior distributions.

Usage

compare_acceptance_diagnostics(
  ...,
  kernel_tol = 1e-12,
  ess_threshold = 20,
  sparse_threshold = 0.1
)

Arguments

...

Posterior model objects or a named list of model objects.

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.

Value

A bsvar_post_tbl combining acceptance diagnostic results across models, with a model column identifying each model specification.

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_s1 <- bsvars::estimate(spec_s, S = 5, show_progress = FALSE)
post_s2 <- bsvars::estimate(spec_s, S = 5, show_progress = FALSE)

comp <- compare_acceptance_diagnostics(m1 = post_s1, m2 = post_s2)
print(comp)
#> # A tibble: 26 × 16
#>    model object_type            metric         value flag  message analysis_type
#>    <chr> <chr>                  <chr>          <dbl> <lgl> <chr>   <chr>        
#>  1 m1    acceptance_diagnostics posterior_dra…     5 FALSE ""      acceptance_d…
#>  2 m1    acceptance_diagnostics effective_sam…     5 TRUE  "ESS b… acceptance_d…
#>  3 m1    acceptance_diagnostics max_tries        Inf FALSE ""      acceptance_d…
#>  4 m1    acceptance_diagnostics irf_sign_rest…     1 FALSE ""      acceptance_d…
#>  5 m1    acceptance_diagnostics zero_restrict…     0 FALSE ""      acceptance_d…
#>  6 m1    acceptance_diagnostics structural_si…     0 FALSE ""      acceptance_d…
#>  7 m1    acceptance_diagnostics narrative_res…     0 FALSE ""      acceptance_d…
#>  8 m1    acceptance_diagnostics kernel_mean        1 FALSE ""      acceptance_d…
#>  9 m1    acceptance_diagnostics kernel_median      1 FALSE ""      acceptance_d…
#> 10 m1    acceptance_diagnostics kernel_min         1 FALSE ""      acceptance_d…
#> # ℹ 16 more rows
#> # ℹ 9 more variables: interval_probability <dbl>, interval_method <chr>,
#> #   scale_by <chr>, scale_var <chr>, scale_factor <dbl>, units <chr>,
#> #   source_package <chr>, source_version <chr>, n_draws <int>
# }