gelman.plot {coda} | R Documentation |
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, auto.layout = TRUE, ask = TRUE, 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 ) |
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. |
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.
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.
Brooks, S P. and Gelman, A. (1998) General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics. 7. p434-455.