Returns a summary of the priors used in the model.
Usage
describe_prior(model, ...)
# S3 method for class 'brmsfit'
describe_prior(model, parameters = NULL, ...)Arguments
- model
A Bayesian model.
- ...
Currently not used.
- parameters
Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like
lp__orprior_) are filtered by default, so only parameters that typically appear in thesummary()are returned. Useparametersto 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 2.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 0.096 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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#> Chain 2: Elapsed Time: 0.049 seconds (Warm-up)
#> Chain 2: 0.045 seconds (Sampling)
#> Chain 2: 0.094 seconds (Total)
#> Chain 2:
#>
#> 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.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: Elapsed Time: 0.046 seconds (Warm-up)
#> Chain 3: 0.044 seconds (Sampling)
#> Chain 3: 0.09 seconds (Total)
#> Chain 3:
#>
#> 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.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Chain 4: Elapsed Time: 0.059 seconds (Warm-up)
#> Chain 4: 0.041 seconds (Sampling)
#> Chain 4: 0.1 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 1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: Elapsed Time: 0.019 seconds (Warm-up)
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#> Chain 1: 0.034 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 3e-06 seconds
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#> Chain 2: Elapsed Time: 0.021 seconds (Warm-up)
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#> Chain 2: 0.037 seconds (Total)
#> Chain 2:
#>
#> 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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#> 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
# }