bayesplot (version 1.3.0)

bayesplot-package: Plots for Bayesian Models

Description

Stan Development Team

The bayesplot package provides a variety of ggplot2-based plotting functions for use after fitting Bayesian models (typically, though not exclusively, via Markov chain Monte Carlo). The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of packages for Bayesian modeling, particularly (but not necessarily) packages powered by rstan. Examples of packages that will soon (or already are) using bayesplot are rstan itself, as well as the rstan-dependent rstanarm and brms packages for applied regression modeling.

Plotting functionality

The plotting functions in bayesplot are organized into several modules:

• MCMC: Visualizations of Markov chain Monte Carlo (MCMC) simulations generated by any MCMC algorithm. There are also additional functions specifically for use with models fit using the No-U-Turn Sampler (NUTS).

• PPC: Graphical posterior predictive checks (PPCs).

• Coming soon: In future releases modules will be added specifically for forecasting/out-of-sample prediction and other inference-related tasks.

Questions, feature requests, bug reports

• Bug reports and feature requests: If you'd like to request a new feature or if you've noticed a bug that needs to be fixed please let us know at the bayesplot issue tracker on GitHub:

  https://github.com/stan-dev/bayesplot/issues/.

• General questions and help: To ask a question about bayesplot on the Stan-users forum please visit

  http://discourse.mc-stan.org.

See Also

theme_default for the default ggplot theme used by bayesplot.

bayesplot-colors to set or view the color scheme used for plotting.

ggsave in ggplot2 for saving plots.

Examples

# NOT RUN {
# A few quick examples (all of the functions have many examples
# on their individual help pages)

# MCMC plots
x <- example_mcmc_draws(params = 5)
mcmc_intervals(x, prob = 0.5)
mcmc_intervals(x, regex_pars = "beta")

color_scheme_set("purple")
mcmc_areas(x, regex_pars = "beta", prob = 0.8)

color_scheme_set("mix-blue-red")
mcmc_trace(x, pars = c("alpha", "sigma"),
           facet_args = list(nrow = 2))

color_scheme_set("brightblue")
mcmc_scatter(x, pars = c("beta[1]", "sigma"),
             transformations = list(sigma = "log"))


# Graphical PPCs
y <- example_y_data()
yrep <- example_yrep_draws()
ppc_dens_overlay(y, yrep[1:50, ])
# }
# NOT RUN {
color_scheme_set("pink")
ppc_stat(y, yrep, stat = "median") + grid_lines()
ppc_hist(y, yrep[1:8, ])
# }
# NOT RUN {
# }