prior creates a prior distribution.
The prior can be visualized by the
prior( distribution, parameters, truncation = list(lower = -Inf, upper = Inf), prior_weights = 1 )
name of the prior distribution. The possible options are
for a point density characterized by a
for a normal distribution characterized
for a lognormal distribution characterized
for a Cauchy distribution characterized
scale parameters. Internally
converted into a generalized t-distribution with
df = 1.
for a generalized t-distribution characterized
for a gamma distribution characterized
scale parameters. The later is internally converted to
for an inverse-gamma distribution
characterized by a
scale parameters. The
JAGS part uses a 1/gamma distribution with a shape and rate
for a beta distribution
characterized by an
for an exponential distribution
characterized by either
parameter. The later is internally converted to
for a uniform distribution defined on a
list of appropriate parameters for a given
list with two elements,
upper, that define the lower and upper truncation of the
distribution. Defaults to
list(lower = -Inf, upper = Inf).
The truncation is automatically set to the bounds of the support.
prior odds associated with a given distribution. The value is passed into the model fitting function, which creates models corresponding to all combinations of prior distributions for each of the model parameters and sets the model priors odds to the product of its prior distributions.
prior_none return an object of class 'prior'.
A named list containing the distribution name, parameters, and prior weights.
# create a standard normal prior distribution p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1)) # create a half-normal standard normal prior distribution p2 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1), truncation = list(lower = 0, upper = Inf)) # the prior distribution can be visualized using the plot function # (see ?plot.prior for all options) plot(p1)