R: Metropolis within Gibbs sampling algorithm of a posterior...

gibbs {LearnBayes} | R Documentation

## Metropolis within Gibbs sampling algorithm of a posterior distribution

### Description

Implements a Metropolis-within-Gibbs sampling algorithm for an arbitrary real-valued posterior density defined by the user

### Usage

    gibbs(logpost,start,m,scale,...)

### Arguments

`logpost` | function defining the log posterior density
`start` | array with a single row that gives the starting value of the parameter vector
`m` | the number of iterations of the chain
`scale` | vector of scale parameters for the random walk Metropolis steps
`...` | data that is used in the function logpost

### Value

`par` | a matrix of simulated values where each row corresponds to a value of the vector parameter
`accept` | vector of acceptance rates of the Metropolis steps of the algorithm

### Author(s)

Jim Albert

### Examples

    data=c(6,2,3,10)
    start=array(c(1,1),c(1,2))
    m=1000
    scale=c(2,2)
    s=gibbs(logctablepost,start,m,scale,data)

[Package LearnBayes version 2.15 Index]