Tidy a fitted bml model

Turns a fitted bml model into a tidy tibble following the broom conventions: one row per parameter with columns term, estimate, std.error, conf.low, and conf.high. Because the model is Bayesian, estimate is the posterior mean, std.error the posterior standard deviation, and conf.low/conf.high the bounds of an equal-tailed credible interval.

With these methods, ecosystem tools that build on broom work out of the box, e.g. modelsummary::modelsummary(list(m1, m2)) for side-by-side regression tables or dotwhisker::dwplot(m1) for coefficient plots.

Usage

# S3 method for class 'bml'
tidy(
  x,
  conf.int = TRUE,
  conf.level = 0.95,
  component = c("all", "fixed", "random", "weights", "fn"),
  ...
)

Arguments

x

A fitted model object of class "bml" returned by bml.

conf.int

Logical. Include credible interval columns conf.low/conf.high? Default: TRUE.

conf.level

Width of the equal-tailed credible interval. Default: 0.95, which is read directly from the fitted model. Other levels are computed from the posterior draws and therefore require the model to be fitted with monitor = TRUE.

component

Which parameters to return:

• "all" (default): all of the below

• "fixed": all class-"b" coefficients (main-formula terms, block features, interactions)

• "random": variance parameters (sd(...), cor(...), sigma, Weibull shape)

• "weights": weight-model parameters (w[...])

• "fn": aggregation-function shape parameters (fn[...])

...

Additional arguments (currently unused).

Value

A tibble with columns term, estimate, std.error, conf.low, conf.high (if conf.int), and component.

See also

bml, glance.bml, bmlCompare, coefPlot, summary.bml

Author

Benjamin Rosche benrosche@nyu.edu

Examples

if (FALSE) { # \dontrun{
data(coalgov)

m1 <- bml(
  Surv(dur_wkb, event_wkb) ~ 1 + majority +
    mm(id = id(pid, gid), vars = vars(cohesion), w = w(~ 1/n), fn = fn("sum"), RE = TRUE),
  family = weibull(),
  data = coalgov
)

tidy(m1)                      # regression coefficients
tidy(m1, component = "all")   # including weight and variance parameters
tidy(m1, conf.level = 0.9)    # 90% credible intervals (requires monitor = TRUE)

# Multi-model comparison table (see also bmlCompare()):
models <- list(base = m1, weighted = m2)
purrr::imap_dfr(models, \(m, name)
  tidy(m) |>
    dplyr::mutate(model = name, N = glance(m)$nobs, DIC = glance(m)$DIC)
)
} # }