geweke.plot((mcmc.obj, frac1 = 0.1, frac2 = 0.5, max.segs = 50, seg.size = 10, auto.layout = T))
frac1
| fraction to use from beginning of chain |
frac2
| fraction to use from end of chain |
max.segs
| Maximum number of segments to use |
seg.size
| Number of observations in each segment |
auto.layout
|
If TRUE then, set up own layout for
plots, otherwise use existing one.
|
geweke
indicates that the first and last part of a sample
from a Markov chain are not drawn from the same distribution, it
may be useful to discard the first few iterations to see if the
rest of the chain has "converged". This plot shows what happens to
Geweke's Z-score when successively larger numbers of iterations
are discarded from the beginning of the chain.
The Markov chain is divided into segments according to the arguments
seg.size
and max.segs
. Then Geweke's Z-score is repeatedly
calculated. The first Z-score is calculated with all iterations in the
chain, the second after discarding the first segment, the third after
discarding the first two segments, and so on. The last Z-score is
calculated using only the samples in the last segment, which always
contains at least 50 observations.
The argument max.segs
overrides the argument seg.size
if the two conflict. Hence for chains with 500 observations or less,
the default bin size is 10. For longer chains the bin size is
determined by splitting the chain into 50 equal-sized bins.
geweke.diag