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Describe Priors

Source: R/describe_prior.R
describe_prior.Rd

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

# \donttest{
library(bayestestR)

# rstanarm models
# -----------------------------------------------
if (require("rstanarm")) {
  model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
  describe_prior(model)
}
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.109 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:                0.107 seconds (Total)
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#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1e-05 seconds
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#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 9e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
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#> Chain 4:  Elapsed Time: 0.068 seconds (Warm-up)
#> Chain 4:                0.046 seconds (Sampling)
#> Chain 4:                0.114 seconds (Total)
#> Chain 4: 
#>     Parameter Prior_Distribution Prior_Location Prior_Scale
#> 1 (Intercept)             normal       20.09062   15.067370
#> 2          wt             normal        0.00000   15.399106
#> 3         cyl             normal        0.00000    8.436748

# brms models
# -----------------------------------------------
if (require("brms")) {
  model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
  describe_prior(model)
}
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 7e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
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#> Chain 2: Gradient evaluation took 3e-06 seconds
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#> Chain 4: 
#>     Parameter Prior_Distribution Prior_Location Prior_Scale Prior_df
#> 1 b_Intercept          student_t           19.2         5.4        3
#> 2        b_wt            uniform             NA          NA       NA
#> 3       b_cyl            uniform             NA          NA       NA
#> 4       sigma          student_t            0.0         5.4        3

# BayesFactor objects
# -----------------------------------------------
if (require("BayesFactor")) {
  bf <- ttestBF(x = rnorm(100, 1, 1))
  describe_prior(bf)
}
#>    Parameter Prior_Distribution Prior_Location Prior_Scale
#> 1 Difference             cauchy              0   0.7071068
# }