An R package providing a library of plotting functions for use after fitting Bayesian models (typically with MCMC). The idea 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.
The plots created by bayesplot are ggplot objects, which means that after a plot is created it can be further customized using the various functions for modifying ggplot objects provided by the ggplot2 package.
- Install from CRAN:
- Install latest development version from GitHub (requires devtools package):
if (!require("devtools")) install.packages("devtools") devtools::install_github("stan-dev/bayesplot", dependencies = TRUE, build_vignettes = TRUE)
If you are not using the RStudio IDE and you get an error related to "pandoc" you will either need to remove the argument
build_vignettes=TRUE (to avoid building the vignettes) or install pandoc (e.g.,
brew install pandoc) and probably also pandoc-citeproc (e.g.,
brew install pandoc-citeproc). If you have the
rmarkdown R package installed then you can check if you have pandoc by running the following in R:
library("bayesplot") library("rstanarm") library("ggplot2") fit <- stan_glm(mpg ~ ., data = mtcars) posterior <- as.matrix(fit) plot_title <- ggtitle("Posterior distributions", "with medians and 80% intervals") mcmc_areas(posterior, pars = c("cyl", "drat", "am", "wt"), prob = 0.8) + plot_title
color_scheme_set("red") ppc_dens_overlay(y = fit$y, yrep = posterior_predict(fit, draws = 50))
# also works nicely with piping library("dplyr") color_scheme_set("brightblue") fit %>% posterior_predict(draws = 500) %>% ppc_stat_grouped(y = mtcars$mpg, group = mtcars$carb, stat = "median")
# with rstan demo model library("rstan") fit2 <- stan_demo("eight_schools", warmup = 300, iter = 700) posterior2 <- extract(fit2, inc_warmup = TRUE, permuted = FALSE) color_scheme_set("mix-blue-pink") p <- mcmc_trace(posterior2, pars = c("mu", "tau"), n_warmup = 300, facet_args = list(nrow = 2, labeller = label_parsed)) p + facet_text(size = 15)
color_scheme_set("red") np <- nuts_params(fit2) mcmc_nuts_energy(np, merge_chains = FALSE) + ggtitle("NUTS Energy Diagnostic")
# another example with rstanarm color_scheme_set("purple") fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars) ppc_intervals( y = mtcars$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")