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Provides a summary of the prior distributions used for the parameters in a given model.

Usage

get_priors(x, ...)

# S3 method for class 'brmsfit'
get_priors(x, verbose = TRUE, ...)

Arguments

x

A Bayesian model.

...

Currently not used.

verbose

Toggle warnings and messages.

Value

A data frame with a summary of the prior distributions used for the parameters in a given model.

Examples

# \donttest{
library(rstanarm)
model <- stan_glm(Sepal.Width ~ Species * Petal.Length, data = iris)
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.454 seconds (Warm-up)
#> Chain 1:                0.503 seconds (Sampling)
#> Chain 1:                0.957 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!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
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#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.459 seconds (Warm-up)
#> Chain 2:                0.508 seconds (Sampling)
#> Chain 2:                0.967 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
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#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.448 seconds (Warm-up)
#> Chain 3:                0.537 seconds (Sampling)
#> Chain 3:                0.985 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
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#> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.415 seconds (Warm-up)
#> Chain 4:                0.493 seconds (Sampling)
#> Chain 4:                0.908 seconds (Total)
#> Chain 4: 
get_priors(model)
#>                        Parameter Distribution Location Scale Adjusted_Scale
#> 1                    (Intercept)       normal 3.057333   2.5      1.0896657
#> 2              Speciesversicolor       normal 0.000000   2.5      2.3038121
#> 3               Speciesvirginica       normal 0.000000   2.5      2.3038121
#> 4                   Petal.Length       normal 0.000000   2.5      0.6172700
#> 5 Speciesversicolor:Petal.Length       normal 0.000000   2.5      0.5360283
#> 6  Speciesvirginica:Petal.Length       normal 0.000000   2.5      0.4119705
# }