describe_prior: Describe Priors

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

Describe Priors

Description

Returns a summary of the priors used in the model.

Usage

describe_prior(model, ...)

## S3 method for class 'brmsfit'
describe_prior(
  model,
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all", "location",
    "distributional", "auxiliary"),
  parameters = NULL,
  ...
)

Arguments

model	A Bayesian model.
...	Currently not used.
effects	Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
component	Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models.
parameters	Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like lp__ or prior_) are filtered by default, so only parameters that typically appear in the summary() are returned. Use parameters to select specific parameters for the output.

Examples

library(bayestestR)

# rstanarm models
# -----------------------------------------------
if (require("rstanarm")) {
  model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
  describe_prior(model)
}

# brms models
# -----------------------------------------------
if (require("brms")) {
  model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
  describe_prior(model)
}

# BayesFactor objects
# -----------------------------------------------
if (require("BayesFactor")) {
  bf <- ttestBF(x = rnorm(100, 1, 1))
  describe_prior(bf)
}

bayestestR documentation built on Oct. 17, 2024, 5:07 p.m.