Summarise a Model Fit

A model fit's metadata paired with a per-term posterior summary table.

Usage

# S3 method for class 'kb_fit'
summary(
  object,
  ...,
  conf_level = 0.95,
  estimate = stats::median,
  sig_fig = 3,
  include_random_effects = FALSE
)

Arguments

object

A kb_fit object.

...

Unused.

conf_level

A number between 0 and 1 giving the compatibility-interval level.

estimate

A function that reduces a numeric vector of posterior draws to a scalar point estimate (e.g. median or mean).

sig_fig

A whole number of significant figures for summary output.

include_random_effects

A flag specifying whether to include the group-level random-effect terms in the output.

Value

A summary_kb_fit object: a list of fit metadata and a coefficients tibble with columns term, estimate, lower, upper, rhat, ess_bulk, and ess_tail.

Details

The print method renders a header (likelihood family, fixed- and random-effect structure, observation and group counts, sampler configuration, and the convergence verdict), the coefficient table, and a diagnostics footer. For a compact overview without the numeric table, call print() on the fit itself.

The coefficient table reports, per term:

estimate

the posterior point estimate (the estimate function; the median by default).

lower, upper

the conf_level equal-tailed compatibility limits.

rhat

the potential scale reduction factor, comparing between- and within-chain variance; values near 1 indicate convergence.

ess_bulk

the bulk effective sample size, governing the reliability of central posterior summaries.

ess_tail

the tail effective sample size, governing the reliability of the interval limits.

Population-level coefficients and random-effect standard deviations are always shown. The group-level deviations are included only when include_random_effects = TRUE, following the convention that summary reports the variance hyperparameters rather than the per-level effects (the latter are the tidy() default).

See also

Other generics: augment.kb_fit(), coef.kb_fit(), converged.kb_fit(), fitted.kb_fit_weight(), glance.kb_fit(), kb_stancode(), log_lik.kb_fit(), posterior_epred.kb_fit_weight(), posterior_linpred.kb_fit_weight(), posterior_predict.kb_fit_weight(), predict.kb_fit_weight(), prior_summary.kb_fit(), residuals.kb_fit_weight(), samples(), tidy.kb_fit_weight()

Examples

summary(fit_weight_sim_nereo)
#> <summary_kb_fit>
#> Model:     weight (nereocystis)
#> Family:    Student-t (df = 4); response log(weight)
#> Fixed:     intercept + linear + quadratic log(diameter/d0)
#> Random:    site (intercept, slope); site:year (intercept)
#> Centered:  log-diameter at d0 = 40.1 (geometric mean of diameter)
#> Data:      234 observations; groups: site (10), site:year (39)
#> Draws:     2 chains, 400 post-warmup draws each (thin = 1), 800 total
#> Converged: TRUE
#> 
#> # A tibble: 7 × 7
#>   term          estimate   lower  upper  rhat ess_bulk ess_tail
#>   <chr>            <dbl>   <dbl>  <dbl> <dbl>    <dbl>    <dbl>
#> 1 bWeight        -1.47   -1.66   -1.24  1.00       249      331
#> 2 bDiameter       2.5     2.28    2.74  1.03       218      444
#> 3 bDiameter2      0.0749 -0.156   0.313 0.998     1176      739
#> 4 sSite           0.27    0.176   0.459 1.00       226      501
#> 5 sSiteDiameter   0.313   0.169   0.62  1.02       201      380
#> 6 sSiteYear       0.0962  0.0521  0.149 1.01       222      356
#> 7 sWeight         0.161   0.141   0.185 1.00       538      561
#> 
#> estimate: posterior point estimate; lower, upper: 95% compatibility limits.
#> rhat: potential scale reduction factor (1 at convergence).
#> ess_bulk, ess_tail: bulk and tail effective sample sizes.
#> 0 divergent transitions.