rp: Random samples from the prior of a 'hyper2' object

In hyper2: The Hyperdirichlet Distribution, Mark 2

Random samples from the prior of a hyper2 object

Description

Uses Metropolis-Hastings to return random samples from the prior of a hyper2 object

Usage

rp(n, H, startp = NULL, fcm = NULL, fcv = NULL, SMALL = 1e-06, l=loglik,...)

Arguments

H	Object of class hyper2
n	Number of samples
startp	Starting value for the Markov chain, with default NULL being interpreted as starting from the evaluate
fcm,fcv	Constraints as for maxp()
SMALL	Notional small value for numerical stability
l	Log-likelihood function with default loglik()
...	Further arguments, currently ignored

Details

Uses the implementation of Metropolis-Hastings from the MCE package to sample from the posterior PDF of a hyper2 object.

If the distribution is Dirichlet, use rdirichlet() to generate random observations: it is much faster, and produces serially independent samples. To return uniform samples, use rp_unif() (documented at dirichlet.Rd).

Value

Returns a matrix, each row being a unit-sum observation.

Note

Function rp() a random sample from a given normalized likelihood function. To return a likelihood function based on random observations, use rhyper2().

Author(s)

Robin K. S. Hankin

See Also

maxp,loglik,dirichlet,rhyper2

Examples

rp(10,icons)

plot(loglik(rp(30,icons),icons),type='b')

hyper2 documentation built on Aug. 21, 2022, 1:05 a.m.