Calculate a basket of summaries from a weighted list of posterior samples

Usage

posterior_summarise(
  posteriors_df,
  priors_list,
  p = c(0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.975)
)

Arguments

posteriors_df: a dataframe of posteriors that have been selected by ABC this may include columns for scores, weight and/or simulation outputs ( abc_component_score, abc_summary_distance, abc_weight, abc_simulation ) as well as columns matching the priors input specification.
priors_list: a named list of priors specified as a abc_prior S3 object (see priors()), this can include derived values as unnamed 2-sided formulae, where the LHS of the formula will be assigned to the value of the RHS, plus optionally a set of constraints as one sided formulae where the RHS of the formulae will resolve to a boolean value.
p: a progressr progress bar

Value

a dataframe indexed by parameter with useful summary metrics.

Examples


fit = example_adaptive_fit()
summ = posterior_summarise(fit$posteriors, fit$priors)

summ %>% dplyr::glimpse()
#> Rows: 3
#> Columns: 9
#> Groups: param [3]
#> $ param     <chr> "mean", "sd1", "sd2"
#> $ mean      <dbl> 4.979629, 2.090683, 1.014513
#> $ sd        <dbl> 0.03592986, 0.06405630, 0.03351014
#> $ quantiles <list> [<tbl_df[9 x 2]>], [<tbl_df[9 x 2]>], [<tbl_df[9 x 2]>]
#> $ q.0.025   <dbl> 4.893898, 1.769493, 0.925236
#> $ q.0.5     <dbl> 4.972857, 2.094028, 1.011725
#> $ q.0.975   <dbl> 5.075677, 2.214940, 1.167002
#> $ density   <distfn[]> [mean; Median (IQR) 4.97 [4.95 — 5.01]], [sd1; Median (IQR) …
#> $ ESS       <dbl> 262.8049, 262.8049, 262.8049