gelman.plot {coda}R Documentation

Gelman-Rubin-Brooks plot

Description

This plot shows the evolution of Gelman and Rubin's shrink factor as the number of iterations increases.

Usage

gelman.plot(mcmc.list.obj, bin.width = 10, max.bins = 50,
confidence = 0.95, transform = FALSE, auto.layout = TRUE, ask = TRUE,
...)

Details

The Markov chain is divided into bins according to the arguments bin.width and max.bins. Then the Gelman-Rubin shrink factor is repeatedly calculated. The first shrink factor is calculated with observations 1:50, the second with observations 1:(50+n) where n is the bin width, the third contains samples 1:(50+2n) and so on.

Theory

A potential problem with gelman.diag is that it may mis-diagnose convergence if the shrink factor happens to be close to 1 by chance. By calculating the shrink factor at several points in time, gelman.plot shows if the shrink factor has really converged, or whether it is still fluctuating.

References

Brooks, S P. and Gelman, A. (1998) General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics. 7. p434-455.

See Also

gelman.diag.


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