mcmc: Markov Chain Monte Carlo Objects

In coda: Output Analysis and Diagnostics for MCMC

Description

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.

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.

Author(s)

Martyn Plummer

See Also

mcmc.list, mcmcUpgrade, thin, window.mcmc, summary.mcmc, plot.mcmc.

coda documentation built on Oct. 24, 2020, 9:10 a.m.