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https://github.com/stan-dev/bayesplot
bayesplot R package for plotting Bayesian models
https://github.com/stan-dev/bayesplot
bayesian ggplot2 mcmc pandoc r-package stan statistical-graphics visualization
Last synced: 1 day ago
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bayesplot R package for plotting Bayesian models
- Host: GitHub
- URL: https://github.com/stan-dev/bayesplot
- Owner: stan-dev
- License: gpl-3.0
- Created: 2016-05-20T20:28:56.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-08-05T19:00:32.000Z (3 months ago)
- Last Synced: 2024-11-06T19:37:56.102Z (7 days ago)
- Topics: bayesian, ggplot2, mcmc, pandoc, r-package, stan, statistical-graphics, visualization
- Language: R
- Homepage: https://mc-stan.org/bayesplot
- Size: 346 MB
- Stars: 433
- Watchers: 37
- Forks: 83
- Open Issues: 57
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# bayesplot 
[](https://cran.r-project.org/web/packages/bayesplot)
[](https://cran.rstudio.com/package=bayesplot)
[](https://github.com/stan-dev/bayesplot/actions)
[](https://codecov.io/gh/stan-dev/bayesplot)
### Overview
**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, graphical posterior (or prior) predictive checking,
and general plots of posterior (or prior) predictive distributions.
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**](https://mc-stan.org/rstan).
### Getting started
If you are just getting started with **bayesplot** we recommend starting with
the tutorial [vignettes](https://mc-stan.org/bayesplot/articles/index.html),
the examples throughout the package [documentation](https://mc-stan.org/bayesplot/reference/index.html),
and the paper _Visualization in Bayesian workflow_:
* Gabry J, Simpson D, Vehtari A, Betancourt M, Gelman A (2019). Visualization in Bayesian workflow.
_J. R. Stat. Soc. A_, 182: 389-402. doi:10.1111/rssa.12378.
([journal version](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378),
[arXiv preprint](https://arxiv.org/abs/1709.01449),
[code on GitHub](https://github.com/jgabry/bayes-vis-paper))
### Resources
* [mc-stan.org/bayesplot](https://mc-stan.org/bayesplot) (online documentation, vignettes)
* [Ask a question](https://discourse.mc-stan.org) (Stan Forums on Discourse)
* [Open an issue](https://github.com/stan-dev/bayesplot/issues) (GitHub issues for bug reports, feature requests)
### Contributing
We are always looking for new contributors! See [CONTRIBUTING.md](https://github.com/stan-dev/bayesplot/blob/master/.github/CONTRIBUTING.md) for details and/or reach out via the issue tracker.
### Installation
* Install from CRAN:
```r
install.packages("bayesplot")
```
* Install latest development version from GitHub (requires [devtools](https://github.com/hadley/devtools) package):
```r
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("stan-dev/bayesplot", dependencies = TRUE, build_vignettes = FALSE)
```
This installation won't include the vignettes (they take some time to build), but all of the vignettes are
available online at [mc-stan.org/bayesplot/articles](https://mc-stan.org/bayesplot/articles/).
### Examples
Some quick examples using MCMC draws obtained from the [__rstanarm__](https://mc-stan.org/rstanarm)
and [__rstan__](https://mc-stan.org/rstann) packages.
```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
```
```r
color_scheme_set("red")
ppc_dens_overlay(y = fit$y,
yrep = posterior_predict(fit, draws = 50))
```
```r
# also works nicely with piping
library("dplyr")
color_scheme_set("brightblue")
fit %>%
posterior_predict(draws = 500) %>%
ppc_stat_grouped(y = mtcars$mpg,
group = mtcars$carb,
stat = "median")
```
```r
# with rstan demo model
library("rstan")
fit2 <- stan_demo("eight_schools", warmup = 300, iter = 700)
posterior2 <- extract(fit2, inc_warmup = TRUE, permuted = FALSE)
color_scheme_set("mix-blue-pink")
p <- mcmc_trace(posterior2, pars = c("mu", "tau"), n_warmup = 300,
facet_args = list(nrow = 2, labeller = label_parsed))
p + facet_text(size = 15)
```
```r
# scatter plot also showing divergences
color_scheme_set("darkgray")
mcmc_scatter(
as.matrix(fit2),
pars = c("tau", "theta[1]"),
np = nuts_params(fit2),
np_style = scatter_style_np(div_color = "green", div_alpha = 0.8)
)
```
```r
color_scheme_set("red")
np <- nuts_params(fit2)
mcmc_nuts_energy(np) + ggtitle("NUTS Energy Diagnostic")
```
```r
# another example with rstanarm
color_scheme_set("purple")
fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars)
ppc_intervals(
y = mtcars$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")
```
