coda (version 0.19-3)

gelman.diag: Gelman and Rubin's convergence diagnostic

Description

The `potential scale reduction factor' is calculated for each variable in x, together with upper and lower confidence limits. Approximate convergence is diagnosed when the upper limit is close to 1. For multivariate chains, a multivariate value is calculated that bounds above the potential scale reduction factor for any linear combination of the (possibly transformed) variables.

The confidence limits are based on the assumption that the stationary distribution of the variable under examination is normal. Hence the `transform' parameter may be used to improve the normal approximation.

Usage

gelman.diag(x, confidence = 0.95, transform=FALSE, autoburnin=TRUE,
                   multivariate=TRUE)

Arguments

x

An mcmc.list object with more than one chain, and with starting values that are overdispersed with respect to the posterior distribution.

confidence

the coverage probability of the confidence interval for the potential scale reduction factor

transform

a logical flag indicating whether variables in x should be transformed to improve the normality of the distribution. If set to TRUE, a log transform or logit transform, as appropriate, will be applied.

autoburnin

a logical flag indicating whether only the second half of the series should be used in the computation. If set to TRUE (default) and start(x) is less than end(x)/2 then start of series will be adjusted so that only second half of series is used.

multivariate

a logical flag indicating whether the multivariate potential scale reduction factor should be calculated for multivariate chains

Value

An object of class gelman.diag. This is a list with the following elements:

psrf

A list containing the point estimates of the potential scale reduction factor (labelled Point est.) and their upper confidence limits (labelled Upper C.I.).

mpsrf

The point estimate of the multivariate potential scale reduction factor. This is NULL if there is only one variable in x.

The gelman.diag class has its own print method.

Theory

Gelman and Rubin (1992) propose a general approach to monitoring convergence of MCMC output in which $m>1$ parallel chains are run with starting values that are overdispersed relative to the posterior distribution. Convergence is diagnosed when the chains have `forgotten' their initial values, and the output from all chains is indistinguishable. The gelman.diag diagnostic is applied to a single variable from the chain. It is based a comparison of within-chain and between-chain variances, and is similar to a classical analysis of variance.

There are two ways to estimate the variance of the stationary distribution: the mean of the empirical variance within each chain, $W$, and the empirical variance from all chains combined, which can be expressed as $${\hat{\sigma }}^2=\frac{(n-1)W}{n}+\frac{B}{n}$$ where $n$ is the number of iterations and $B/n$ is the empirical between-chain variance.

If the chains have converged, then both estimates are unbiased. Otherwise the first method will underestimate the variance, since the individual chains have not had time to range all over the stationary distribution, and the second method will overestimate the variance, since the starting points were chosen to be overdispersed.

The convergence diagnostic is based on the assumption that the target distribution is normal. A Bayesian credible interval can be constructed using a t-distribution with mean $$\hat{\mu }=\text{Sample mean of all chainscombined}$$ and variance $$\hat{V}={\hat{\sigma }}^2+\frac{B}{mn}$$ and degrees of freedom estimated by the method of moments $$d=\frac{2\ast {\hat{V}}^2}{\text{Var}(\hat{V})}$$ Use of the t-distribution accounts for the fact that the mean and variance of the posterior distribution are estimated.

The convergence diagnostic itself is $$R=\sqrt{\frac{(d+3)\hat{V}}{(d+1)W}}$$ Values substantially above 1 indicate lack of convergence. If the chains have not converged, Bayesian credible intervals based on the t-distribution are too wide, and have the potential to shrink by this factor if the MCMC run is continued.

References

Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-511.

Brooks, SP. and Gelman, A. (1998) General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434-455.

See Also

gelman.plot.