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Summarize a fitted bml model

Source: R/summary.bml.R
summary.bml.Rd

S3 method for summarizing bml model objects. Returns a formatted table of parameter estimates with posterior means, standard deviations, and credible intervals, along with model information and convergence statistics.

Usage

# S3 method for class 'bml'
summary(object, r = 3, diagnostics = TRUE, ...)

Arguments

object

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

r

Number of decimal places for rounding numeric output. Default: 3.

diagnostics

Logical; include per-parameter R-hat and effective sample sizes in the printed table? Default: TRUE.

...

Additional arguments (currently unused).

Value

A data frame of class "bml_summary" containing rounded parameter estimates with the following columns:

  • Parameter: Labeled parameter names

  • Estimate: Posterior mean

  • Est.Error: Posterior standard deviation

  • Q2.5/Q97.5: 95% credible interval

  • Rhat, ESS_bulk, and ESS_tail: convergence diagnostics when diagnostics = TRUE

The object includes metadata attributes printed above the table:

  • Outcome family and link function

  • Estimate type (posterior mean from MCMC)

  • Credible interval specification (95% equal-tailed)

  • Level specification (mm and hm block details)

  • DIC (Deviance Information Criterion) for model comparison

Details

The summary method rounds all numeric values for readability while preserving the underlying structure and metadata from the fitted model. All columns remain accessible via standard data frame indexing (e.g., $Parameter, $Estimate).

For Cox models with piecewise baseline hazards (when cox_intervals is specified), the outcome description includes the number of intervals used.

See also

bml, tidy.bml, coefPlot, monetPlot, mcmcDiag

Author

Benjamin Rosche <benrosche@nyu.edu>

Examples

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

# Fit model
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) +
    hm(id = id(cid)),
  family = weibull(),
  data = coalgov
)

# View summary
summary(m1)

# Summary with more decimal places
summary(m1, r = 4)

# Access specific columns
s <- summary(m1)
s$Parameter  # Parameter names
s$Estimate   # Posterior means
s$Q2.5       # Lower credible bounds

# Custom posterior summaries (requires monitor = "parameters")
draws <- posterior::as_draws_df(m1)

# Select specific parameters and compute custom summaries
draws |>
  dplyr::select(dplyr::starts_with("b[")) |>
  tidyr::pivot_longer(everything(), names_to = "param") |>
  dplyr::group_by(param) |>
  dplyr::summarise(
    median = median(value),
    mad    = mad(value),
    q05    = quantile(value, 0.05),
    q95    = quantile(value, 0.95)
  )
} # }