Summarize mu and tau hyperparameters

Computes posterior summaries for the hierarchical hyperparameters (mu, tau, and tau_squared). Returns a data frame with one row per parameter containing posterior means, standard deviations, quantiles, and convergence diagnostics.

This is a focused alternative to summary(fit), which returns summaries for all parameters including theta.

Usage

summarise_mu_tau(fit, probs = c(0.025, 0.5, 0.975), measures = NULL)

summarize_mu_tau(fit, probs = c(0.025, 0.5, 0.975), measures = NULL)

Arguments

fit

A shrinkr_fit object from shrink().

probs

Numeric vector of quantiles to compute. Default is c(0.025, 0.5, 0.975) for 95% credible intervals.

measures

Optional character vector or list of summary measures to compute. If NULL, uses mean, sd, and convergence diagnostics.

Value

A data frame (tibble if available) with one row per parameter and columns:

parameter

Parameter name (mu, tau, or tau_squared)

mean

Posterior mean

sd

Posterior standard deviation

q2.5, q50, q97.5

Quantiles (or custom quantiles from probs)

rhat

R-hat convergence diagnostic

ess_bulk

Effective sample size (bulk)

ess_tail

Effective sample size (tail)

See also

shrink() for fitting models, extract_mu_tau() for raw hyperparameter draws, summarise_theta() for group-level summaries

Examples

set.seed(1)
draws <- data.frame(
  mu = rnorm(20, 0.2, 0.05),
  tau = abs(rnorm(20, 0.3, 0.03)),
  `theta[1]` = rnorm(20, 0.0, 0.1),
  `theta[2]` = rnorm(20, 0.3, 0.1),
  `theta[3]` = rnorm(20, 0.5, 0.1),
  check.names = FALSE
)
draws$tau_squared <- draws$tau^2
fit <- list(
  fit = posterior::as_draws_df(draws),
  data = list(
    G = 3, K = 1, centered = FALSE,
    vars = c("group1", "group2", "group3"),
    quantiles = data.frame(
      q2.5 = c(-0.20, 0.10, 0.30),
      q50 = c(0.00, 0.30, 0.50),
      q97.5 = c(0.20, 0.50, 0.70)
    )
  ),
  summary = posterior::summarise_draws(
    posterior::as_draws_df(draws),
    "mean", "sd",
    ~posterior::quantile2(., probs = c(0.025, 0.5, 0.975))
  ),
  diagnostics = list(n_divergent = 0, max_treedepth = 0, n_leapfrog = 0)
)
class(fit) <- "shrinkr_fit"
summarise_mu_tau(fit)
#> # A tibble: 3 × 6
#>   parameter     mean     sd   q2.5    q50 q97.5
#>   <chr>        <dbl>  <dbl>  <dbl>  <dbl> <dbl>
#> 1 mu          0.210  0.0457 0.122  0.218  0.278
#> 2 tau         0.300  0.0261 0.248  0.298  0.337
#> 3 tau_squared 0.0905 0.0153 0.0614 0.0890 0.114
summarise_mu_tau(fit, probs = c(0.05, 0.5, 0.95))
#> # A tibble: 3 × 6
#>   parameter     mean     sd     q5    q50   q95
#>   <chr>        <dbl>  <dbl>  <dbl>  <dbl> <dbl>
#> 1 mu          0.210  0.0457 0.155  0.218  0.276
#> 2 tau         0.300  0.0261 0.255  0.298  0.333
#> 3 tau_squared 0.0905 0.0153 0.0651 0.0890 0.111