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(x, bin.width = 10, max.bins = 50,
confidence = 0.95, transform = FALSE, auto.layout = TRUE, ask = TRUE,
col, lty, xlab, ylab, type, ...)

Arguments

x an mcmc object
bin.width Number of observations per segment, excluding the first segment which always has at least 50 iterations.
max.bins Maximum number of bins, excluding the last one.
confidence Coverage probability of confidence interval.
transform Automatic variable transformation (see gelman.diag)
auto.layout If TRUE then, set up own layout for plots, otherwise use existing one.
ask Prompt user before displaying each page of plots.
col graphical parameter (see par)
lty graphical parameter (see par)
xlab graphical parameter (see par)
ylab graphical parameter (see par)
type graphical parameter (see par)
... further graphical parameters.

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.


[Package coda version 0.8-3 Index]