Extract prior draws of specified parameters
Usage
# S3 method for class 'brmsfit'
prior_draws(x, variable = NULL, pars = NULL, ...)
prior_draws(x, ...)
prior_samples(x, ...)Arguments
- x
An
Robject typically of classbrmsfit.- variable
A character vector providing the variables to extract. By default, all variables are extracted.
- pars
Deprecated alias of
variable. For reasons of backwards compatibility,parsis interpreted as a vector of regular expressions by default unlessfixed = TRUEis specified.- ...
Arguments passed to individual methods (if applicable).
Details
To make use of this function, the model must contain draws of
prior distributions. This can be ensured by setting sample_prior =
TRUE in function brm. Priors of certain parameters cannot be saved
for technical reasons. For instance, this is the case for the
population-level intercept, which is only computed after fitting the model
by default. If you want to treat the intercept as part of all the other
regression coefficients, so that sampling from its prior becomes possible,
use ... ~ 0 + Intercept + ... in the formulas.
Examples
# \dontrun{
fit <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = "cumulative",
prior = set_prior("normal(0,2)", class = "b"),
sample_prior = TRUE)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000308 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.08 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000241 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.41 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000242 seconds
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#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 0.000236 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 2.36 seconds.
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#> Chain 4:
# extract all prior draws
draws1 <- prior_draws(fit)
head(draws1)
#> Intercept b sd_subject
#> 1 -0.6914414 1.03966220 0.04305431
#> 2 0.4100858 -1.30219209 0.88032498
#> 3 -0.6499643 -4.22614007 4.23688415
#> 4 1.3236922 -2.28570696 3.99322064
#> 5 5.6541591 -0.09990052 14.30585129
#> 6 -8.0518432 3.43212761 3.95786077
# extract prior draws for the coefficient of 'treat'
draws2 <- prior_draws(fit, "b_treat")
head(draws2)
#> b_treat
#> 1 -2.1192972
#> 2 1.5051579
#> 3 -0.5706313
#> 4 -4.8309096
#> 5 6.7716104
#> 6 -0.6459806
# }