report.brmsfit: Reporting Bayesian Models from brms

In report: Automated Reporting of Results and Statistical Models

Reporting Bayesian Models from brms

Description

Create reports for Bayesian models. The description of the parameters follows the Sequential Effect eXistence and sIgnificance Testing framework (see SEXIT documentation).

Usage

## S3 method for class 'brmsfit'
report(x, ...)

## S3 method for class 'brmsfit'
report_effectsize(x, effectsize_method = "basic", ...)

Arguments

x	Object of class lm or glm.
...	Arguments passed to or from other methods.
effectsize_method	Method for computing effect sizes. For brmsfit objects, defaults to "basic" (faster, no refitting) instead of "refit" to improve performance with large Bayesian models. See documentation for effectsize::effectsize().

Details

Message from the rstan package: "To avoid recompilation of unchanged Stan programs, we recommend calling rstan_options(auto_write = TRUE)"

Value

An object of class report().

See Also

Specific components of reports (especially for stats models):

• report_table()

• report_parameters()

• report_statistics()

• report_effectsize()

• report_model()

• report_priors()

• report_random()

• report_performance()

• report_info()

• report_text()

Other types of reports:

• report_system()

• report_packages()

• report_participants()

• report_sample()

• report_date()

Methods:

• as.report()

Template file for supporting new models:

• report.default()

Examples

# Bayesian models
library(brms)
model <- suppressWarnings(brm(mpg ~ qsec + wt,
  data = mtcars,
  refresh = 0, iter = 300
))
r <- report(model, verbose = FALSE)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

report documentation built on Nov. 5, 2025, 7:38 p.m.