Batteries-included plots

Batteries-included plotting functions are available at the arviz_plots top level namespace and provide plug and play opinionated solutions to common tasks within the Bayesian workflow.

Each of the entries below describe the behaviour of each function, all its arguments and include a handful of examples of each. A complementary introduction and guide to plot_... functions is available at Introduction to batteries-included plots.

combine_plots(dt, plots[, var_names, ...])	Arrange multiple batteries-included plots in a customizable column or row layout.
plot_autocorr(dt[, var_names, filter_vars, ...])	Autocorrelation plots for the given dataset.
plot_bf(dt, var_names[, ref_val, kind, ...])	Bayes Factor for comparing hypothesis of two nested models.
plot_compare(cmp_df[, similar_shade, ...])	Summary plot for model comparison.
plot_convergence_dist(dt[, diagnostics, ...])	Plot the distribution of convergence diagnostics (ESS and/or R-hat).
plot_dist(dt[, var_names, filter_vars, ...])	Plot 1D marginal densities in the style of John K.
plot_ecdf_pit(dt[, var_names, filter_vars, ...])	Plot Δ-ECDF.
plot_energy(dt[, bfmi, kind, ...])	Plot transition distribution and marginal energy distribution in HMC algorithms.
plot_ess(dt[, var_names, filter_vars, ...])	Plot effective sample size plots.
plot_ess_evolution(dt[, var_names, ...])	Plot estimated effective sample size plots for increasing number of iterations.
plot_forest(dt[, var_names, filter_vars, ...])	Plot 1D marginal credible intervals in a single plot.
plot_loo_pit(dt[, ci_prob, coverage, ...])	LOO-PIT Δ-ECDF values with simultaneous confidence envelope.
plot_mcse(dt[, var_names, filter_vars, ...])	Plot Monte Carlo standard error.
plot_pair(dt[, var_names, filter_vars, ...])	Plot all variables against each other in the dataset.
plot_pair_focus(dt, focus_var[, ...])	Plot a fixed variable against other variables in the dataset.
plot_parallel(dt[, var_names, filter_vars, ...])	Plot parallel coordinates plot showing posterior points with and without divergences.
plot_ppc_dist(dt[, data_pairs, var_names, ...])	Plot 1D marginals for the posterior/prior predictive distribution and the observed data.
plot_ppc_pava(dt[, data_type, n_bootstaps, ...])	PAV-adjusted calibration plot.
plot_ppc_pit(dt[, ci_prob, coverage, ...])	PIT Δ-ECDF values with simultaneous confidence envelope.
plot_ppc_rootogram(dt[, ci_prob, yscale, ...])	Rootogram with confidence intervals per predicted count.
plot_ppc_tstat(dt[, var_names, group, ...])	Plot Bayesian t-stat for observed data and posterior/prior predictive.
plot_prior_posterior(dt[, var_names, ...])	Plot 1D marginal densities for prior and posterior.
plot_psense_dist(dt[, alphas, var_names, ...])	Plot power scaled posteriors.
plot_psense_quantities(dt[, alphas, ...])	Plot power scaled posterior quantities.
plot_rank(dt[, var_names, filter_vars, ...])	Fractional rank Δ-ECDF plots.
plot_rank_dist(dt[, var_names, filter_vars, ...])	Plot 1D marginal distributions and fractional rank Δ-ECDF plots.
plot_ridge(dt[, var_names, filter_vars, ...])	Plot 1D marginal densities in a single plot, akin to a forest plot.
plot_trace(dt[, var_names, filter_vars, ...])	Plot iteration versus sampled values.
plot_trace_dist(dt[, var_names, ...])	Plot 1D marginal distributions and iteration versus sampled values.