`MCMC-overview.Rd`

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:

**3-D array**: An array with dimensions`[Iteration, Chain, Parameter]`

in that order.**list**: A list of matrices, where each matrix corresponds to a Markov chain. All of the matrices should have the same number of iterations (rows) and parameters (columns), and parameters should have the same names and be in the same order.**matrix (2-D array)**: A matrix with one column per parameter. If using matrix there should only be a single Markov chain or all chains should already be merged (stacked).**data frame**: There are two types of data frames allowed. Either a data frame with one column per parameter (if only a single chain or all chains have already been merged), or a data frame with one column per parameter plus an additional column`"Chain"`

that contains the chain number (an integer) corresponding to each row in the data frame.

**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.

Posterior distributions: Histograms and kernel density plots of parameter draws, optionally showing each Markov chain separately.

Uncertainty intervals: Uncertainty intervals computed from parameter draws.

Trace plots: Times series of parameter draws, optionally including with HMC/NUTS diagnostic information.

Scatterplots: Scatterplots, heatmaps, and pairs plots of parameter draws, optionally including with HMC/NUTS diagnostic information.

Parallel coordinates plots: Parallel coordinates plot of MCMC draws (one dimension per parameter), optionally including with HMC/NUTS diagnostic information.

Combos: Combination plots (e.g. trace plot + histogram).

General MCMC diagnostics: MCMC diagnostic plots including R-hat, effective sample size, autocorrelation. NUTS diagnostics: Special diagnostic plots for the No-U-Turn Sampler.

Comparisons to "true" values: Plots comparing MCMC estimates to "true" parameter values (e.g., values used to simulate data).

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)