density.prior {BayesTools} R Documentation

## Prior density

### Description

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

### Usage

## S3 method for class '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: linlinear transformation in form of a + b*x tanhalso known as Fisher's z transformation expexponential 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 ... additional arguments

### Value

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

prior()