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

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

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`

)

autoburnin

Remove first half of sequence (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. Default is
`dev.interactive()`

in R and `interactive()`

in S-PLUS.

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.

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.

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 \(1:(50+bin.width)\),
the third contains samples \(1:(50+2*bin.width)\) and so on.
If the chain has less than \(50 + bin.width\) iterations then
`gelman.diag`

will exit with an error.

Brooks, S P. and Gelman, A. (1998) General Methods for Monitoring
Convergence of Iterative Simulations. *Journal of Computational and
Graphical Statistics*, **7**, 434-455.