dhyperdirichlet: Probability density function for, and random sampling from, the hyperdirichlet distribution

Description

Probability density function for the hyperdirichlet distribution in terms of either $p$ or $e$; and random sampling using Metropolis-Hastings

Usage

dhyperdirichlet_e(e, HD, include.Jacobian = TRUE)
dhyperdirichlet(p, HD, include.NC = FALSE, TINY = 1e-10, log = FALSE)
rhyperdirichlet(n, HD, start=NULL, sigma=NULL)

Arguments

Object of class hyperdirichlet, or coerced thereto

Vector of length dim(HD), notionally summing to one

Vector of length dim(HD) giving the point in e-space

include.Jacobian

In function dhyperdiriclet_e(), Boolean with default TRUE meaning to include the Jabobian of the transform from e to p

include.NC

In function dhyperdirichlet_e(), Boolean with TRUE meaning to include the normalization factor and default FALSE meaning not to include it (it is expensive to calculate). Note that if the normalizing fac

TINY

In function dhyperdirichlet_p(), numeric, specifying minimum size for elements of p via

p <-
      pmax(p , TINY)

log

In function dhyperdirichlet_p(), Boolean with default FALSE meaning to return the probability density and TRUE meaning to return its logarithm

n,start,sigma

In function rhyperdirichlet(), n is the number of observations to take, start is the start-point for the random walk (with default NULL meaning to use the neutral point), and sigma

Value

Functions dhyperdirichlet() and dhyperdirichlet_e() return a scalar; function rhyperdirichlet() returns a matrix whose rows are $k$-tuples

Details

Function dhyperdirichlet() gives the density as a function of the $p_1,p_2,\ldots,p_d$.

Function dhyperdirichlet_e() gives the density as a function of the $e_i$. This is useful when integrating as the simplex (in p-space) transforms to a hypercube in e-space.

Examples

Run this code

dhyperdirichlet(c(1,4,3,2)/10, dirichlet(1:4))

rhyperdirichlet(20, dirichlet(1:3))

diff(c(0,sort(runif(9)),1))  # random sample drawn from dirichlet(rep(1,10))