Computes density of a prior distribution across a range of values.

# S3 method for prior
density(
x,
x_seq = NULL,
x_range = NULL,
x_range_quant = NULL,
n_points = 1000,
n_samples = 10000,
force_samples = FALSE,
individual = FALSE,
transformation = NULL,
transformation_arguments = NULL,
transformation_settings = FALSE,
truncate_end = TRUE,
...
)

Arguments

x

a prior

x_seq

sequence of x coordinates

x_range

vector of length two with lower and upper range for the support (used if x_seq is unspecified)

x_range_quant

quantile used for automatically obtaining x_range if both x_range and x_seq are unspecified. Defaults to 0.005 for all but Cauchy, Student-t, Gamma, and Inverse-gamme distributions that use 0.010.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

individual

should individual densities be returned (e.g., in case of weightfunction)

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

truncate_end

whether the density should be set to zero in for the endpoints of truncated distributions

...

density.prior returns an object of class 'density'.
prior()