prior_samples.brmsfit {brms} | R Documentation |
Extract prior samples of specified parameters
## S3 method for class 'brmsfit' prior_samples(x, pars = NA, ...) prior_samples(x, pars = NA, ...)
x |
An |
pars |
Names of parameters for which prior samples should be returned, as given by a character vector or regular expressions. By default, all prior samples are extracted |
... |
Currently ignored |
To make use of this function, the model must contain samples 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.
A data frame containing the prior samples.
## Not run: fit <- brm(rating ~ treat + period + carry + (1|subject), data = inhaler, family = "cumulative", prior = set_prior("normal(0,2)", class = "b"), sample_prior = TRUE) # extract all prior samples samples1 <- prior_samples(fit) head(samples1) # extract prior samples for the population-level effects of 'treat' samples2 <- prior_samples(fit, "b_treat") head(samples2) ## End(Not run)