Geweke-Brooks plot

Usage

geweke.plot((mcmc.obj, frac1 = 0.1, frac2 = 0.5, bin.width = 10,
max.bins = 50, auto.layout = TRUE, ask = TRUE, ...))

Arguments

frac1 fraction to use from beginning of chain.
frac2 fraction to use from end of chain.
bin.width Number of iterations per segment, not counting the last segment which always has at least 50 iterations.
max.bins Maximum number of segments, excluding the last one.
auto.layout If TRUE then, set up own layout for plots, otherwise use existing one.
ask Prompt user before displaying each page of plots
... Graphical parameters

Description

If 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 bin.width and max.bins. 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.

theory

The obvious danger in discarding iterations is that the diagnostic test loses power. In particular, using the default parameters, the last Z-score is based on a comparison of one sample of 5 observations and another sample of 25 observations, which cannot be regarded as a large sample.

Note

The graphical implementation of Geweke's diagnostic was suggested by Steve Brooks.

See Also

geweke.diag


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