Baconvergence | R Documentation |
Test how well-mixed and converged the MCMC runs are with the chosen core and settings, by running the core several times and comparing the different runs using the Gelman and Rubin Reduction factor (Brooks and Gelman, 1998).
Baconvergence(core = "MSB2K", runs = 5, suggest = FALSE, verbose = TRUE, ...)
core |
Name of the core, given using quotes. Defaults to one of the cores provided with rbacon, |
runs |
Amount of runs to test for mixing. Default |
suggest |
If initial analysis of the data indicates abnormally slow or fast accumulation rates, Bacon will suggest to change the prior. |
verbose |
Provide feedback on what is happening (default |
... |
additional options that can be given to the Bacon function. |
Generally Bacon will perform millions of MCMC iterations for each age-model run, although only a fraction of these will be stored. In most cases the remaining MCMC iterations will be well mixed (the upper left panel of the fit of the iterations shows no strange features such as sudden systematic drops or rises). However if the iterations seem not well mixed, or if too few remain (say less than a few hundred), then you could check the Gelman and Rubin Reduction Factor. Too high differences (high Factors) between runs indicate poor MCMC mixing. Robust MCMC mixing is indicated by a Gelman and Rubin Reduction factor (Brooks and Gelman, 1998) below the 1.05 safety threshold.
NA
Maarten Blaauw, J. Andres Christen
Brooks, SP. and Gelman, A. (1998) General methods for monitoring convergence of iterative simulations. _Journal of Computational and Graphical Statistics_, *7*, 434-455.
Baconvergence(runs=2, ssize=100, coredir=tempfile()) # a quick-and-dirty toy example
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.