boa.randl: Raftery and Lewis Convergence Diagnostics

In brian-j-smith/boa: Bayesian Output Analysis Program (BOA) for MCMC

Description

Computes the Raftery and Lewis convergence diagnostics for the parameters in an MCMC sequence.

Usage

boa.randl(link, q, error, prob, delta)

Arguments

link	Matrix whose columns and rows contain the monitored parameters and the MCMC iterations, respectively. The iteration numbers and parameter names must be assigned to dimnames(link).
q	Quantile to be estimated.
error	Desired amount of error in estimating the specified quantile 'q'.
prob	Probability of attaining the desired degree of error - 'error'.
delta	Delta value used in computing the convergence diagnostics.

Value

A matrix whose columns and rows are the Raftery and Lewis convergence diagnostics (i.e. thin, burn-in, total, lower bound, and dependence factor) and the monitored parameters, respectively.

Author(s)

Brian J. Smith, Nicky Best, Kate Cowles

References

1. Raftery, A. L. and Lewis, S. (1992a). Comment: One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Statistical Science, 7, 493-7.

2. Raftery, A. L. and Lewis, S. (1992b). How many iterations in the Gibbs sampler? In Bayesian Statistics 4, (ed. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith), pp. 763-74. Oxford University Press.

See Also

boa.print.randl

brian-j-smith/boa documentation built on May 12, 2019, 5:43 a.m.