Description Usage Arguments Note Author(s) See Also

The function `mcmc`

is used to create a Markov Chain Monte Carlo
object. The input data are taken to be a vector, or a matrix with
one column per variable.

If the optional arguments `start`

, `end`

, and `thin`

are omitted then the chain is assumed to start with iteration 1 and
have thinning interval 1. If `data`

represents a chain that
starts at a later iteration, the first iteration in the chain should
be given as the `start`

argument. Likewise, if `data`

represents a chain that has already been thinned, the thinning
interval should be given as the `thin`

argument.

An mcmc object may be summarized by the `summary`

function
and visualized with the `plot`

function.

MCMC objects resemble time series (`ts`

) objects and have
methods for the generic functions `time`

, `start`

,
`end`

, `frequency`

and `window`

.

1 2 3 |

`data` |
a vector or matrix of MCMC output |

`start` |
the iteration number of the first observation |

`end` |
the iteration number of the last observation |

`thin` |
the thinning interval between consecutive observations |

`x` |
An object that may be coerced to an mcmc object |

`...` |
Further arguments to be passed to specific methods |

The format of the mcmc class has changed between coda version 0.3
and 0.4. Older mcmc objects will now cause `is.mcmc`

to
fail with an appropriate warning message. Obsolete mcmc objects can
be upgraded with the `mcmcUpgrade`

function.

Martyn Plummer

`mcmc.list`

,
`mcmcUpgrade`

,
`thin`

,
`window.mcmc`

,
`summary.mcmc`

,
`plot.mcmc`

.

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