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.
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).
# with rstan demo model library("rstan") fit2 <- stan_demo("eight_schools", warmup = 300, iter = 700) posterior2 <- extract(fit2, inc_warmup = TRUE, permuted = FALSE) color_scheme_set("mix-blue-pink") p <- mcmc_trace(posterior2, pars = c("mu", "tau"), n_warmup = 300, facet_args = list(nrow = 2, labeller = label_parsed)) p + facet_text(size = 15)
# 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")