# mcmc

##### Markov Chain Monte Carlo Objects

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`

.

- Keywords
- ts

##### Usage

```
mcmc(data= NA, start = 1, end = numeric(0), thin = 1)
as.mcmc(x, …)
is.mcmc(x)
```

##### Arguments

- 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

##### Note

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.

##### See Also

`mcmc.list`

,
`mcmcUpgrade`

,
`thin`

,
`window.mcmc`

,
`summary.mcmc`

,
`plot.mcmc`

.

*Documentation reproduced from package coda, version 0.19-3, License: GPL (>= 2)*