Description Usage Arguments Details Value Note Author(s) See Also Examples
Uses MetropolisHastings to return random samples from the prior of a
hyper2
object
1 
H 
Object of class 
n 
Number of samples 
startp 
Starting value for the Markov chain, with default

fcm,fcv 
Constraints as for 
SMALL 
Notional small value for numerical stability 
l 
Loglikelihood function with default 
... 
Further arguments, currently ignored 
Uses the implementation of MetropolisHastings 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
).
Returns a matrix, each row being a unitsum observation.
Function rp()
a random sample from a given normalized
likelihood function. To return a likelihood function based on random
observations, use rhyper2()
.
Robin K. S. Hankin
1 2 3 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.