Performs a simple test to check whether the prior is informative to the posterior. This idea, and the accompanying heuristics, were discussed in this blogpost.
Arguments
- model
A
stanreg
,stanfit
,brmsfit
,blavaan
, orMCMCglmm
object.- method
Can be
"gelman"
or"lakeland"
. For the"gelman"
method, if the SD of the posterior is more than 0.1 times the SD of the prior, then the prior is considered as informative. For the"lakeland"
method, the prior is considered as informative if the posterior falls within the95%
HDI of the prior.- simulate_priors
Should prior distributions be simulated using
simulate_prior()
(default; faster) or sampled viaunupdate()
(slower, more accurate).- ...
Currently not used.
Value
A data frame with two columns: The parameter names and the quality
of the prior (which might be "informative"
, "uninformative"
)
or "not determinable"
if the prior distribution could not be
determined).
Examples
# \dontrun{
library(bayestestR)
if (require("rstanarm")) {
model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
check_prior(model, method = "gelman")
check_prior(model, method = "lakeland")
# An extreme example where both methods diverge:
model <- stan_glm(mpg ~ wt,
data = mtcars[1:3, ],
prior = normal(-3.3, 1, FALSE),
prior_intercept = normal(0, 1000, FALSE),
refresh = 0
)
check_prior(model, method = "gelman")
check_prior(model, method = "lakeland")
# can provide visual confirmation to the Lakeland method
plot(si(model, verbose = FALSE))
}
# }