Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

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: 4 months ago
JSON representation

bayesplot R package for plotting Bayesian models

Lists

README 

        
# bayesplot 



[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/bayesplot?color=blue)](https://cran.r-project.org/web/packages/bayesplot)

[![Downloads](https://cranlogs.r-pkg.org/badges/bayesplot?color=blue)](https://cran.rstudio.com/package=bayesplot)

[![R-CMD-check](https://github.com/stan-dev/bayesplot/workflows/R-CMD-check/badge.svg)](https://github.com/stan-dev/bayesplot/actions)

[![codecov](https://codecov.io/gh/stan-dev/bayesplot/branch/master/graph/badge.svg)](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")

```