Get summary of priors used for a model

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

# \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 2.3e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup) #> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup) #> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 1:  #> Chain 1: Elapsed Time: 0.406 seconds (Warm-up) #> Chain 1: 0.389 seconds (Sampling) #> Chain 1: 0.795 seconds (Total) #> Chain 1:  #>  #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2). #> Chain 2:  #> Chain 2: Gradient evaluation took 1.2e-05 seconds #> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.12 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) #> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 2:  #> Chain 2: Elapsed Time: 0.388 seconds (Warm-up) #> Chain 2: 0.377 seconds (Sampling) #> Chain 2: 0.765 seconds (Total) #> Chain 2:  #>  #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3). #> Chain 3:  #> Chain 3: Gradient evaluation took 1.2e-05 seconds #> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.12 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) #> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 3:  #> Chain 3: Elapsed Time: 0.409 seconds (Warm-up) #> Chain 3: 0.432 seconds (Sampling) #> Chain 3: 0.841 seconds (Total) #> Chain 3:  #>  #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4). #> Chain 4:  #> Chain 4: Gradient evaluation took 1.2e-05 seconds #> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.12 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) #> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 4:  #> Chain 4: Elapsed Time: 0.401 seconds (Warm-up) #> Chain 4: 0.445 seconds (Sampling) #> Chain 4: 0.846 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 # }