MCMC-overview {bayesplot}R Documentation

Plots for Markov chain Monte Carlo simulations


The bayesplot MCMC module provides various plotting functions for creating graphical displays of Markov chain Monte Carlo (MCMC) simulations. The MCMC plotting functions section, below, provides links to the documentation for various categories of MCMC plots. Currently the MCMC plotting functions accept posterior draws provided in one of the following formats:

Note: typically the user should not include warmup iterations in the object passed to bayesplot plotting functions, although for certain plots (e.g. trace plots) it can occasionally be useful to include the warmup iterations for diagnostic purposes.

MCMC plotting functions


Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. doi:10.1111/rssa.12378. (journal version, arXiv preprint, code on GitHub)

See Also

Other MCMC: MCMC-combos, MCMC-diagnostics, MCMC-distributions, MCMC-intervals, MCMC-nuts, MCMC-parcoord, MCMC-recover, MCMC-scatterplots, MCMC-traces

[Package bayesplot version 1.11.1 Index]