Summarize theta parameters by group

Computes posterior summaries for subgroup effects (theta parameters). Returns a data frame with one row per group containing posterior means, standard deviations, quantiles, and convergence diagnostics.

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

Usage

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

summarize_theta(
  fit,
  probs = c(0.025, 0.5, 0.975),
  group_names = NULL,
  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.

group_names

Optional character vector of length G to label groups. If NULL, uses names from fit$data$vars or defaults to "group1", etc.

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 group and columns:

group

Group identifier

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, summarise_mu_tau() for hyperparameter summaries, theta_contrasts() for computing contrasts

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_theta(fit)
#> # A tibble: 3 × 6
#>   group    mean     sd   q2.5    q50 q97.5
#>   <chr>   <dbl>  <dbl>  <dbl>  <dbl> <dbl>
#> 1 group1 0.0139 0.0810 -0.109 0.0114 0.172
#> 2 group2 0.310  0.105   0.146 0.305  0.529
#> 3 group3 0.512  0.0911  0.359 0.530  0.641
summarise_theta(fit, probs = c(0.05, 0.5, 0.95))
#> # A tibble: 3 × 6
#>   group    mean     sd     q5    q50   q95
#>   <chr>   <dbl>  <dbl>  <dbl>  <dbl> <dbl>
#> 1 group1 0.0139 0.0810 -0.105 0.0114 0.146
#> 2 group2 0.310  0.105   0.172 0.305  0.518
#> 3 group3 0.512  0.0911  0.371 0.530  0.623
summarise_theta(fit, group_names = c("Control", "A", "B"))
#> # A tibble: 3 × 6
#>   group     mean     sd   q2.5    q50 q97.5
#>   <chr>    <dbl>  <dbl>  <dbl>  <dbl> <dbl>
#> 1 Control 0.0139 0.0810 -0.109 0.0114 0.172
#> 2 A       0.310  0.105   0.146 0.305  0.529
#> 3 B       0.512  0.0911  0.359 0.530  0.641