bayesplot is an R package providing an extensive library of plotting functions for use after fitting Bayesian models (typically with MCMC). Currently bayesplot offers a variety of plots of posterior draws, visual MCMC diagnostics, as well as graphical posterior predictive checking. Additional functionality (e.g. for forecasting/out-of-sample prediction and other inference-related tasks) will be added in future releases.
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.
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.
- mc-stan.org/bayesplot (online documentation, vignettes)
- Ask a question at The Stan Forums
- File an issue on GitHub (bug reports, feature requests)
- Install from CRAN:
install.packages("bayesplot")- 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:
rmarkdown::pandoc_available()Some quick examples using MCMC draws obtained from the rstanarm and rstan packages.
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$carb,
stat = "median")
# with rstan demo model
library("rstan")
fit2 <- stan_demo("eight_schools", warmup = 300, iter = 700)
posterior2 <- extract(fit2, inc_warmup = TRUE, permuted = FALSE)
color_scheme_set("mix-blue-pink")
p <- mcmc_trace(posterior2, pars = c("mu", "tau"), n_warmup = 300,
facet_args = list(nrow = 2, labeller = label_parsed))
p + facet_text(size = 15)
# scatter plot also showing divergences
color_scheme_set("darkgray")
mcmc_scatter(
as.matrix(fit2),
pars = c("tau", "theta[1]"),
np = nuts_params(fit2),
np_style = scatter_style_np(div_color = "green", div_alpha = 0.8)
)
color_scheme_set("red")
np <- nuts_params(fit2)
mcmc_nuts_energy(np) + ggtitle("NUTS Energy Diagnostic")
# another example with rstanarm
color_scheme_set("purple")
fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars)
ppc_intervals(
y = mtcars$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")