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.
the coverage probability of the confidence interval for the potential scale reduction factor
a logical flag indicating whether variables in
a logical flag indicating whether only the second half
of the series should be used in the computation. If set to TRUE
a logical flag indicating whether the multivariate potential scale reduction factor should be calculated for multivariate chains
An object of class
gelman.diag. This is a list with the
A list containing the point estimates of the potential
scale reduction factor (labelled
The point estimate of the multivariate potential scale reduction
factor. This is NULL if there is only one variable in
gelman.diag class has its own
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
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
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
sigma.hat^2 = (n-1)W/n + 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
mu.hat = Sample mean of all chains combined
V.hat=sigma.hat2 + B/(mn)
and degrees of freedom estimated by the method of moments
d = 2*V.hat^2/Var(V.hat)
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((d+3) V.hat /((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.
The multivariate a version of Gelman and Rubin's diagnostic was proposed by Brooks and Gelman (1998). Unlike the univariate proportional scale reduction factor, the multivariate version does not include an adjustment for the estimated number of degrees of freedom.
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.
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