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Priors of brms models

Source: R/priors.R
prior_summary.brmsfit.Rd

Extract priors of models fitted with brms.

Usage

# S3 method for class 'brmsfit'
prior_summary(object, all = TRUE, ...)

Arguments

object

An object of class brmsfit.

all

Logical; Show all parameters in the model which may have priors (TRUE) or only those with proper priors (FALSE)?

...

Further arguments passed to or from other methods.

Value

An brmsprior object.

Examples

# \dontrun{
fit <- brm(
  count ~ zAge + zBase * Trt + (1|patient) + (1|obs),
  data = epilepsy, family = poisson(),
  prior = prior(student_t(5,0,10), class = b) +
    prior(cauchy(0,2), class = sd)
)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 4.007 seconds (Warm-up)
#> Chain 1:                2.173 seconds (Sampling)
#> Chain 1:                6.18 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 3.679 seconds (Warm-up)
#> Chain 2:                2.191 seconds (Sampling)
#> Chain 2:                5.87 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 3.923 seconds (Warm-up)
#> Chain 3:                2.194 seconds (Sampling)
#> Chain 3:                6.117 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 4.229 seconds (Warm-up)
#> Chain 4:                2.172 seconds (Sampling)
#> Chain 4:                6.401 seconds (Total)
#> Chain 4: 

prior_summary(fit)
#>                   prior     class       coef   group resp dpar nlpar lb ub tag
#>  student_t(3, 1.4, 2.5) Intercept                                             
#>     student_t(5, 0, 10)         b                                             
#>     student_t(5, 0, 10)         b       Trt1                                  
#>     student_t(5, 0, 10)         b       zAge                                  
#>     student_t(5, 0, 10)         b      zBase                                  
#>     student_t(5, 0, 10)         b zBase:Trt1                                  
#>            cauchy(0, 2)        sd                                     0       
#>            cauchy(0, 2)        sd                obs                  0       
#>            cauchy(0, 2)        sd  Intercept     obs                  0       
#>            cauchy(0, 2)        sd            patient                  0       
#>            cauchy(0, 2)        sd  Intercept patient                  0       
#>        source
#>       default
#>          user
#>  (vectorized)
#>  (vectorized)
#>  (vectorized)
#>  (vectorized)
#>          user
#>  (vectorized)
#>  (vectorized)
#>  (vectorized)
#>  (vectorized)
prior_summary(fit, all = FALSE)
#>                   prior     class coef group resp dpar nlpar lb ub tag  source
#>  student_t(3, 1.4, 2.5) Intercept                                      default
#>     student_t(5, 0, 10)         b                                         user
#>            cauchy(0, 2)        sd                             0           user
print(prior_summary(fit, all = FALSE), show_df = FALSE)
#> Intercept ~ student_t(3, 1.4, 2.5)
#> b ~ student_t(5, 0, 10)
#> <lower=0> sd ~ cauchy(0, 2)
# }