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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 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:                0.102 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 9e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 9e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.09 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: 0.064 seconds (Warm-up)
#> Chain 4:                0.044 seconds (Sampling)
#> Chain 4:                0.108 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 8e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.037 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
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#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
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#> Chain 3: Gradient evaluation took 3e-06 seconds
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
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#> Chain 4: Gradient evaluation took 8e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
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#> Chain 4:  Elapsed Time: 0.022 seconds (Warm-up)
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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
# }