Plotting for Bayesian Models (original) (raw)

bayesplot

Plotting Bayesian models

bayesplot is an R package providing an extensive library of plotting functions for use after fitting Bayesian models (typically with MCMC). The plots created by bayesplot are ggplot objects, which means that after a plot is created it can be further customized using various functions from the ggplot2 package.

Currently bayesplot offers a variety of plots of posterior draws, visual MCMC diagnostics, and graphical posterior (or prior) predictive checking. Additional functionality (e.g. for forecasting/out-of-sample prediction and other inference-related tasks) will be added in future releases.

The idea behind bayesplot is 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) those powered by RStan.

Installation

Install the latest release from CRAN:

Install the latest development version from GitHub:

Installation from GitHub does not include the vignettes by default because they take some time to build, but the vignettes can always be accessed online anytime at mc-stan.org/bayesplot/articles).

Examples

Some quick examples using MCMC draws obtained from our rstanarm and rstan packages.

# another example with rstanarm
color_scheme_set("purple")

fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars)
ppc_intervals(
  y = mtcars$mpg,
  yrep = posterior_predict(fit),
  x = mtcars$wt,
  prob = 0.5
) +
  labs(
    x = "Weight (1000 lbs)",
    y = "MPG",
    title = "50% posterior predictive intervals \nvs observed miles per gallon",
    subtitle = "by vehicle weight"
  ) +
  panel_bg(fill = "gray95", color = NA) +
  grid_lines(color = "white")