R: Random walk Metropolis algorithm of a posterior distribution

rwmetrop {LearnBayes} | R Documentation

## Random walk Metropolis algorithm of a posterior distribution

### Description

Simulates iterates of a random walk Metropolis chain for an arbitrary real-valued posterior density defined by the user

### Usage

    rwmetrop(logpost,proposal,start,m,...)

### Arguments

`logpost` | function defining the log posterior density
`proposal` | a list containing var, an estimated variance-covariance matrix, and scale, the Metropolis scale factor
`start` | vector containing the starting value of the parameter
`m` | the number of iterations of the chain
`...` | 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` | the acceptance rate of the algorithm

### Author(s)

Jim Albert

### Examples

    data=c(6,2,3,10)
    varcov=diag(c(1,1))
    proposal=list(var=varcov,scale=2)
    start=array(c(1,1),c(1,2))
    m=1000
    s=rwmetrop(logctablepost,proposal,start,m,data)

[Package LearnBayes version 2.15 Index]