set_bin_prior | R Documentation |
p
Constructs 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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