| set_bin_prior | R Documentation |
pConstructs a prior distribution for use as the argument bin_prior in
rpost or in binpost. The user can choose
from a list of in-built priors or specify their own prior function,
returning the log of the prior density, using an R function
and arguments for hyperparameters.
set_bin_prior(
prior = c("jeffreys", "laplace", "haldane", "beta", "mdi", "northrop"),
...
)
prior |
Either
|
... |
Further arguments to be passed to the user-supplied or in-built
prior function. For the latter this is only relevant if
|
Binomial priors. The names of the binomial priors set using
bin_prior are:
"jeffreys": the Jeffreys beta(1/2, 1/2) prior.
"laplace": the Bayes-Laplace beta(1, 1) prior.
"haldane": the Haldane beta(0, 0) prior.
"beta": a beta(\alpha, \beta) prior. The argument
ab is a vector containing c(\alpha, \beta).
The default is ab = c(1, 1).
"mdi": the MDI prior
\pi(p) = 1.6186 p^p (1-p)^{1-p},
for 0 < p < 1.
"northrop": the improper prior
\pi(p)=\{-\ln(1-p)\}^{-1}(1-p)^{-1},
for 0 < p < 1.
Apart from the last two priors these are all beta distributions.
A list of class "binprior". The first component is the
name of the input prior. Apart from the MDI prior this will be "beta",
in which case the other component of the list is a vector of length two
giving the corresponding values of the beta parameters.
binpost for sampling from a binomial posterior
distribution.
bp <- set_bin_prior(prior = "jeffreys")
# Setting the Jeffreys prior by hand
beta_prior_fn <- function(p, ab) {
return(stats::dbeta(p, shape1 = ab[1], shape2 = ab[2], log = TRUE))
}
jeffreys <- set_bin_prior(beta_prior_fn, ab = c(1 / 2, 1 / 2))
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