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https://github.com/mjskay/tidybayes

Bayesian analysis + tidy data + geoms (R package)
https://github.com/mjskay/tidybayes

bayesian-data-analysis brms ggplot2 jags r r-package stan tidy-data visualization

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Bayesian analysis + tidy data + geoms (R package)

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README 

        
---

output: github_document

---



```{r chunk_options, include=FALSE}

knitr::opts_chunk$set(

  fig.path = "man/figures/README/"

)

knitr::opts_chunk$set(

  fig.retina = 2

)

if (requireNamespace("ragg", quietly = TRUE)) {

  knitr::opts_chunk$set(

    dev = "ragg_png"

  )

} else if (capabilities("cairo")) {

  knitr::opts_chunk$set(

    dev = "png",

    dev.args = list(png = list(type = "cairo"))

  )

}

dir.create("README_models", showWarnings = FALSE)

```



# tidybayes: Bayesian analysis + tidy data + geoms



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[![DOI](https://zenodo.org/badge/33396684.svg)](https://zenodo.org/badge/latestdoi/33396684)



![Preview of tidybayes plots](man/figures/preview.gif)



[tidybayes](https://mjskay.github.io/tidybayes/) is an R package that aims to make it easy to integrate popular Bayesian 

modeling methods into a tidy data + ggplot workflow. It builds on top of (and re-exports)

several functions for visualizing uncertainty from its sister package, 

[ggdist](https://mjskay.github.io/ggdist/)



[Tidy](https://dx.doi.org/10.18637/jss.v059.i10)

data frames (one observation per row) are particularly convenient for use

in a variety of R data manipulation and visualization packages. However,

when using Bayesian modeling functions like JAGS or Stan in R, we often have

to translate this data into a form the model understands, and then after

running the model, translate the resulting sample (or predictions) into a more tidy

format for use with other R functions.  `tidybayes` aims to simplify these 

two common (often tedious) operations:



* __Composing data__ for use with the model. This often means translating

  data from a `data.frame` into a `list` , making sure `factors` are encoded as

  numerical data, adding variables to store the length of indices, etc. This

  package helps automate these operations using the `compose_data()` function, which

  automatically handles data types like `numeric`, `logical`, `factor`, and `ordinal`, 

  and allows easy extensions for converting other data types into a format the

  model understands by providing your own implementation of the generic `as_data_list()`.



* __Extracting tidy draws__ from the model. This often means extracting indices

  from parameters with names like `"b[1,1]"`, `"b[1,2]"` into separate columns

  of a data frame, like `i = c(1,1,..)` and `j = c(1,2,...)`. More tediously,

  sometimes these indices actually correspond to levels of a factor in the original

  data; e.g. `"x[1]"` might correspond to a value of `x` for the first level of

  some factor. We provide several straightforward ways to convert draws from a

  variable with indices into useful long-format 

  ("[tidy](https://dx.doi.org/10.18637/jss.v059.i10)") 

  data frames, with automatic back-conversion of common data types (factors, logicals)

  using the `spread_draws()` and `gather_draws()` functions, including automatic 

  recovery of factor levels corresponding to variable indices. In most cases this

  kind of long-format data is much easier to use with other data-manipulation and 

  plotting packages (e.g., `dplyr`, `tidyr`, `ggplot2`) than the format provided 

  by default from the model. See `vignette("tidybayes")` for examples.



`tidybayes` also provides some additional functionality for data manipulation

and visualization tasks common to many models:



* __Extracting tidy fits and predictions__ from models. For models like those

  provided by `rstanarm` and `brms`, `tidybayes` provides a tidy analog of the

  `posterior_epred()`, `posterior_predict()`, and `posterior_linpred()` functions,

  called `add_epred_draws()`, `add_predicted_draws()`, and `add_linpred_draws()`.

  These functions are modeled after the `modelr::add_predictions()`

  function, and turn a grid of predictions into a long-format data frame of

  draws from either the fits or predictions from a model. These functions make

  it straightforward to generate arbitrary fit lines from a model. See

  `vignette("tidy-brms")` or `vignette("tidy-rstanarm")` for examples.

  

* __Summarizing posterior distributions__ from models. `tidybayes` re-exports the 

  `ggdist::point_interval()` family of functions (`median_qi()`, `mean_qi()`, `mode_hdi()`, etc),

  which are methods for generating point summaries and intervals that are designed with tidy workflows 

  in mind. They can generate point summaries plus an arbitrary number of probability 

  intervals *from* tidy data frames of draws, they *return* tidy data frames,

  and they **respect data frame groups**. `tidybayes` also provides and implementation

  of `posterior::summarise_draws()` for use with grouped data frames, such as those

  returned by the `tidybayes::XXX_draws` functions.



* __Visualizing priors and posteriors__. The focus on tidy data makes the output from tidybayes

  easy to visualize using `ggplot`. While existing `geom`s (like `ggdist::geom_pointrange()` and 

  `ggdist::geom_linerange()`) can give useful output, the output from `tidybayes` is designed to work

  well with several geoms and stats in its sister package, `ggdist`. These geoms have sensible

  defaults suitable for visualizing posterior point summaries and intervals (`ggdist::geom_pointinterval()`,

  `ggdist::stat_pointinterval()`), visualizing

  distributions with point summaries and intervals (the `ggdist::stat_sample_slabinterval()` family of

  stats, including eye plots, half-eye plots, CCDF bar plots, gradient plots, dotplots, and histograms), 

  and visualizing fit lines with an arbitrary number of uncertainty bands (`ggdist::geom_lineribbon()` 

  and `ggdist::stat_lineribbon()`). Priors can also be visualized in the same way using the 

  `ggdist::stat_slabinterval()` family of stats. The `ggdist::geom_dotsinterval()` family also

  automatically finds good binning parameters for dotplots, and can be used to easily construct

  quantile dotplots of posteriors (see example in this document). For convenience, `tidybayes`

  re-exports the `ggdist` stats and geoms.

  

  ![The slabinterval family of geoms and stats](man/figures/slabinterval_family.png)



  See `vignette("slabinterval", package = "ggdist")` for more information.



* __Extracting and visualizing data frames of random variables__ from models.

  `tidybayes` also provides `XXX_rvars` functions as alternatives to the 

  `XXX_draws` functions, such as `spread_rvars()`, `add_predicted_rvars()`, etc. 

  These functions instead return tidy data frames of `posterior::rvar()`s, a

  vectorized random variable data type (see `vignette("rvar", package = "posterior")`

  for more about `rvar`s). Combined with the `ggdist::stat_slabinterval()`

  and `ggdist::stat_lineribbon()` geometries, these functions make it

  easy to extract samples from distributions, manipulate them, and visualize them;

  this format may have significant advantages in terms of memory required for 

  large models. See `vignette("tidy-posterior")` for examples.



* __Comparing a variable across levels of a factor__, which often means first

  generating pairs of levels of a factor (according to some desired set of 

  comparisons) and then computing a function over the value of the comparison

  variable for those pairs of levels. Assuming your data is in the format

  returned by `spread_draws`, the `compare_levels` function allows comparison

  across levels to be made easily.



Finally, `tidybayes` aims to fit into common workflows through __compatibility with

other packages__:



* Its core functions for returning tidy data frames of draws are built on top

  of `posterior::as_draws_df()`.



* Drop-in functions to translate tidy column names used by `tidybayes` to/from names used

  by other common packages and functions, including column names used by

  `ggmcmc::ggs` (via `to_ggmcmc_names` and `from_ggmcmc_names`) and column names used by

  `broom::tidy` (via `to_broom_names` and `from_broom_names`), which makes comparison

  with results of other models straightforward.

  

* The `unspread_draws` and `ungather_draws` functions invert

  `spread_draws` and `gather_draws`, aiding compatibility with other Bayesian

  plotting packages (notably `bayesplot`).

  

* The `gather_emmeans_draws` function turns the output from `emmeans::emmeans`

  (formerly `lsmeans`) into long-format data frames (when applied to supported

  model types, like `MCMCglmm` and `rstanarm` models).



  



## Supported model types



`tidybayes` aims to support a variety of models with a uniform interface. Currently supported models include 

[rstan](https://cran.r-project.org/package=rstan),

[cmdstanr](https://mc-stan.org/cmdstanr/),

[brms](https://cran.r-project.org/package=brms), 

[rstanarm](https://cran.r-project.org/package=rstanarm), 

[runjags](https://cran.r-project.org/package=runjags),

[rjags](https://cran.r-project.org/package=rjags), 

[jagsUI](https://cran.r-project.org/package=jagsUI), 

[coda::mcmc and coda::mcmc.list](https://cran.r-project.org/package=coda),

[posterior::draws](https://mc-stan.org/posterior/),

[MCMCglmm](https://cran.r-project.org/package=MCMCglmm), 

and anything with its own `as.mcmc.list` implementation. If you install the [tidybayes.rethinking](https://mjskay.github.io/tidybayes.rethinking/) package, models from the [rethinking](https://github.com/rmcelreath/rethinking) package are also supported.



## Installation



You can install the currently-released version from CRAN with this R

command:



```{r install, eval=FALSE}

install.packages("tidybayes")

```



Alternatively, you can install the latest development version from GitHub with these R

commands:



```{r install_github, eval=FALSE}

install.packages("devtools")

devtools::install_github("mjskay/tidybayes")

```



## Examples



This example shows the use of tidybayes with the Stan modeling language; however, tidybayes supports many other model types, such as JAGS, brm, rstanarm, and (theoretically) any model type supported by `coda::as.mcmc.list`.



```{r setup, message = FALSE, warning = FALSE}

library(magrittr)

library(dplyr)

library(ggplot2)

library(rstan)

library(tidybayes)

library(emmeans)

library(broom)

library(brms)

library(modelr)

library(forcats)

library(cowplot)

library(RColorBrewer)

library(gganimate)



theme_set(theme_tidybayes() + panel_border())

```



```{r hidden_options, include=FALSE}

rstan_options(auto_write = TRUE)

options(mc.cores = parallel::detectCores())



#misc options

options(width = 90)

```



Imagine this dataset:



```{r make_data}

set.seed(5)

n = 10

n_condition = 5

ABC =

  tibble(

    condition = factor(rep(c("A","B","C","D","E"), n)),

    response = rnorm(n * 5, c(0,1,2,1,-1), 0.5)

  )



ABC %>%

  ggplot(aes(x = response, y = condition)) +

  geom_point(alpha = 0.5) +

  ylab("condition")

```



A hierarchical model of this data might fit an overall mean across the conditions (`overall_mean`), the standard deviation of the condition means (`condition_mean_sd`), the mean within each condition (`condition_mean[condition]`) and the standard deviation of the responses given a condition mean (`response_sd`):



```{stan abc_model, output.var = "ABC_stan", results = "hide", cache = TRUE}

data {

  int n;

  int n_condition;

  int condition[n];

  real response[n];

}

parameters {

  real overall_mean;

  vector[n_condition] condition_zoffset;

  real response_sd;

  real condition_mean_sd;

}

transformed parameters {

  vector[n_condition] condition_mean;

  condition_mean = overall_mean + condition_zoffset * condition_mean_sd;

}

model {

  response_sd ~ cauchy(0, 1);         // => half-cauchy(0, 1)

  condition_mean_sd ~ cauchy(0, 1);   // => half-cauchy(0, 1)

  overall_mean ~ normal(0, 5);

  condition_zoffset ~ normal(0, 1);   // => condition_mean ~ normal(overall_mean, condition_mean_sd)

  for (i in 1:n) {

    response[i] ~ normal(condition_mean[condition[i]], response_sd);

  }

}

```



### Composing data for input to model: `compose_data`



We have compiled and loaded this model into the variable `ABC_stan`. Rather than munge the data into a format Stan likes ourselves, we will use the `tidybayes::compose_data()` function, which takes our `ABC` data frame and automatically generates a list of the following elements:



* `n`: number of observations in the data frame

* `n_condition`: number of levels of the condition factor

* `condition`: a vector of integers indicating the condition of each observation

* `response`: a vector of observations



So we can skip right to modeling:



```{r abc_sampling}

m = sampling(ABC_stan, data = compose_data(ABC), control = list(adapt_delta = 0.99))

```



### Getting tidy draws from the model: `spread_draws`



We decorate the fitted model using `tidybayes::recover_types()`, which will ensure that numeric indices (like `condition`) are back-translated back into factors when we extract data:



```{r recover_types}

m %<>% recover_types(ABC)

```



Now we can extract variables of interest using `spread_draws`, which automatically parses indices, converts them back into their original format, and turns them into data frame columns. This function accepts a symbolic specification of Stan variables using the same syntax you would to index columns in Stan. For example, we can extract the condition means and the residual standard deviation:



```{r spread_draws}

m %>%

  spread_draws(condition_mean[condition], response_sd) %>%

  head(15)  # just show the first few rows

```



The condition numbers are automatically turned back into text ("A", "B", "C", ...) and split into their own column. A long-format data frame is returned with a row for every draw $\times$ every combination of indices across all variables given to `spread_draws`; for example, because `response_sd` here is not indexed by `condition`, within the same draw it has the same value for each row corresponding to a different `condition` (some other formats supported by `tidybayes` are discussed in `vignette("tidybayes")`; in particular, the format returned by `gather_draws`).



### Plotting posteriors as eye plots: `stat_eye()`



Automatic splitting of indices into columns makes it easy to plot the condition means here. We will employ the `ggdist::stat_eye()` geom, which combines a violin plot of the posterior density, median, 66% and 95% quantile interval to give an "eye plot" of the posterior. The point and interval types are customizable using the `point_interval()` family of functions. A "half-eye" plot (non-mirrored density) is also available as `ggdist::stat_halfeye()`. All tidybayes geometries automatically detect their appropriate orientation, though this can be overridden with the `orientation` parameter if the detection fails.



```{r stat_eye}

m %>%

  spread_draws(condition_mean[condition]) %>%

  ggplot(aes(x = condition_mean, y = condition)) +

  stat_eye()

```



Or one can employ the similar "half-eye" plot:



```{r stat_halfeye}

m %>%

  spread_draws(condition_mean[condition]) %>%

  ggplot(aes(x = condition_mean, y = condition)) +

  stat_halfeye()

```



A variety of other stats and geoms for visualizing priors and posteriors are available; see `vignette("slabinterval", package = "ggdist")` for an overview of them.



### Plotting posteriors as quantile dotplots



Intervals are nice if the alpha level happens to line up with whatever decision you are trying to make, but getting a shape of the posterior is better (hence eye plots, above). On the other hand, making inferences from density plots is imprecise (estimating the area of one shape as a proportion of another is a hard perceptual task). Reasoning about probability in frequency formats is easier, motivating [quantile dotplots](https://github.com/mjskay/when-ish-is-my-bus/blob/master/quantile-dotplots.md) ([Kay et al. 2016](https://doi.org/10.1145/2858036.2858558), [Fernandes et al. 2018](https://doi.org/10.1145/3173574.3173718)), which also allow precise estimation of arbitrary intervals (down to the dot resolution of the plot, 100 in the example below). 



Within the slabinterval family of geoms in tidybayes is the `dots` and `dotsinterval` family, which automatically determine appropriate bin sizes for dotplots and can calculate quantiles from samples to construct quantile dotplots. `ggdist::stat_dots()` is the variant designed for use on samples:



```{r quantile_dotplots}

m %>%

  spread_draws(condition_mean[condition]) %>%

  ggplot(aes(x = condition_mean, y = condition)) +

  stat_dots(quantiles = 100) 

```



The idea is to get away from thinking about the posterior as indicating one canonical point or interval, but instead to represent it as (say) 100 approximately equally likely points.



### Point and interval summaries



The functions `ggdist::median_qi()`, `ggdist::mean_qi()`, `ggdist::mode_hdi()`, etc (the `point_interval` functions) give tidy output of point summaries and intervals:



```{r median_qi}

m %>%

  spread_draws(condition_mean[condition]) %>%

  median_qi(condition_mean)

```



### Comparison to other models via compatibility with `broom`



Translation functions like `ggdist::to_broom_names()`, `ggdist::from_broom_names()`, `ggdist::to_ggmcmc_names()`, etc. can be used to translate between common tidy format data frames with different naming schemes. This makes it easy, for example, to compare points summaries and intervals between `tidybayes` output and models that are supported by `broom::tidy`.



For example, let's compare against ordinary least squares (OLS) regression:



```{r broom_ols}

linear_results = 

  lm(response ~ condition, data = ABC) %>% 

  emmeans(~ condition) %>% 

  tidy(conf.int = TRUE) %>%

  mutate(model = "OLS")

linear_results

```



Using `ggdist::to_broom_names()`, we'll convert the output from `median_qi` (which uses names `.lower` and `.upper`) to use names from `broom` (`conf.low` and `conf.high`) so that comparison with output from `broom::tidy` is easy:



```{r broom_tidybayes}

bayes_results = m %>%

  spread_draws(condition_mean[condition]) %>%

  median_qi(estimate = condition_mean) %>%

  to_broom_names() %>%

  mutate(model = "Bayes")

bayes_results

```



This makes it easy to bind the two results together and plot them:



```{r broom_bind}

bind_rows(linear_results, bayes_results) %>%

  ggplot(aes(y = condition, x = estimate, xmin = conf.low, xmax = conf.high, color = model)) +

  geom_pointinterval(position = position_dodge(width = .3))

```



Shrinkage towards the overall mean is visible in the Bayesian results.



### Posterior prediction and complex custom plots



The tidy data format returned by `spread_draws` also facilitates additional computation on variables followed by the construction of more complex custom plots. For example, we can generate posterior predictions easily, and use the `.width` argument (passed internally to `median_qi`) to generate any number of intervals from the posterior predictions, then plot them alongside point summaries and the data:



```{r pp_intervals}

m %>%

  spread_draws(condition_mean[condition], response_sd) %>%

  mutate(prediction = rnorm(n(), condition_mean, response_sd)) %>%

  ggplot(aes(y = condition)) +

  

  # posterior predictive intervals

  stat_interval(aes(x = prediction), .width = c(.5, .8, .95)) +

  scale_color_brewer() +

  

  # median and quantile intervals of condition mean

  stat_pointinterval(aes(x = condition_mean), .width = c(.66, .95), position = position_nudge(y = -0.2)) +

  

  # data

  geom_point(aes(x = response), data = ABC)

```



This plot shows 66% and 95% quantile credible intervals of posterior median for each condition (point + black line); 95%, 80%, and 50% posterior predictive intervals (blue); and the data.



### Fit curves



For models that support it (like `rstanarm` and `brms` models), We can also use the `add_epred_draws()` or `add_predicted_draws()` functions to generate distributions of posterior means or predictions. Combined with the functions from the `modelr` package, this makes it easy to generate fit curves.



Let's fit a slightly naive model to miles per gallon versus horsepower in the `mtcars` dataset:



```{r m_mpg, results = "hide", message = FALSE, warning = FALSE, cache = TRUE}

m_mpg = brm(

  mpg ~ log(hp), 

  data = mtcars, 

  family = lognormal,



  file = "README_models/m_mpg.rds" # cache model (can be removed)  

)

```



Now we will use `modelr::data_grid`, `tidybayes::add_predicted_draws()`, and `ggdist::stat_lineribbon()` to generate a fit curve with multiple probability bands:



```{r pp_bands}

mtcars %>%

  data_grid(hp = seq_range(hp, n = 101)) %>%

  add_predicted_draws(m_mpg) %>%

  ggplot(aes(x = hp, y = mpg)) +

  stat_lineribbon(aes(y = .prediction), .width = c(.99, .95, .8, .5), color = "#08519C") +

  geom_point(data = mtcars, size = 2) +

  scale_fill_brewer()

```



`ggdist::stat_lineribbon(aes(y = .prediction), .width = c(.99, .95, .8, .5))` is one of several shortcut geoms that simplify common combinations of `tidybayes` functions and `ggplot` geoms. It is roughly equivalent to the following:



```r

  stat_summary(

    aes(y = .prediction, fill = forcats::fct_rev(ordered(after_stat(.width))), group = -after_stat(.width)), 

    geom = "ribbon", point_interval = median_qi, fun.args = list(.width = c(.99, .95, .8, .5))

  ) +

  stat_summary(aes(y = .prediction), fun.y = median, geom = "line", color = "red", linewidth = 1.25)

```



Because this is all tidy data, if you wanted to build a model with interactions among different categorical variables (say a different curve for automatic and manual transmissions), you can easily generate predictions faceted over that variable (say, different curves for different transmission types). Then you could use the existing faceting features built in to ggplot to plot them.



Such a model might be:



```{r m_mpg_am, results = "hide", message = FALSE, warning = FALSE, cache = TRUE}

m_mpg_am = brm(

  mpg ~ log(hp) * am, 

  data = mtcars, 

  family = lognormal,



  file = "README_models/m_mpg_am.rds" # cache model (can be removed)  

)

```



Then we can generate and plot predictions as before (differences from above are highlighted as comments):



```{r pp_bands_facet}

mtcars %>%

  data_grid(hp = seq_range(hp, n = 101), am) %>%    # add am to the prediction grid

  add_predicted_draws(m_mpg_am) %>%

  ggplot(aes(x = hp, y = mpg)) +

  stat_lineribbon(aes(y = .prediction), .width = c(.99, .95, .8, .5), color = "#08519C") +

  geom_point(data = mtcars) +

  scale_fill_brewer() +

  facet_wrap(~ am)                                  # facet by am

```



Or, if you would like overplotted posterior fit lines, you can instead use `tidybayes::add_epred_draws()` to get draws from conditional means (expectations of the posterior predictive, thus `epred`), select some reasonable number of them (say `ndraws = 100`), and then plot them:



```{r spaghetti}

mtcars %>%

  data_grid(hp = seq_range(hp, n = 200), am) %>%

  # NOTE: this shows the use of ndraws to subsample within add_epred_draws()

  # ONLY do this IF you are planning to make spaghetti plots, etc.

  # NEVER subsample to a small sample to plot intervals, densities, etc.

  add_epred_draws(m_mpg_am, ndraws = 100) %>%   # sample 100 means from the posterior

  ggplot(aes(x = hp, y = mpg)) +

  geom_line(aes(y = .epred, group = .draw), alpha = 1/20, color = "#08519C") +

  geom_point(data = mtcars) +

  facet_wrap(~ am)

```



Animated hypothetical outcome plots (HOPs) can also be easily constructed by using `gganimate`:



```{r hops}

set.seed(12345)

ndraws = 50



p = mtcars %>%

  data_grid(hp = seq_range(hp, n = 50), am) %>%

  # NOTE: this shows the use of ndraws to subsample within add_epred_draws()

  # ONLY do this IF you are planning to make spaghetti plots, etc.

  # NEVER subsample to a small sample to plot intervals, densities, etc.

  add_epred_draws(m_mpg_am, ndraws = ndraws) %>%

  ggplot(aes(x = hp, y = mpg)) +

  geom_line(aes(y = .epred, group = .draw), color = "#08519C") +

  geom_point(data = mtcars) +

  facet_wrap(~ am, labeller = label_both) +

  transition_states(.draw, 0, 1) +

  shadow_mark(past = TRUE, future = TRUE, alpha = 1/20, color = "gray50")



animate(p, nframes = ndraws, fps = 2.5, width = 672, height = 480, units = "px", res = 100, dev = "ragg_png")

```



See `vignette("tidybayes")` for a variety of additional examples and more explanation of how it works.



## Feedback, issues, and contributions



I welcome feedback, suggestions, issues, and contributions! Contact me at . If you have found a bug, please file it [here](https://github.com/mjskay/tidybayes/issues/new) with minimal code to reproduce the issue. Pull requests should be filed against the [`dev`](https://github.com/mjskay/tidybayes/tree/dev) branch.



`tidybayes` grew out of helper functions I wrote to make my own analysis pipelines tidier. Over time it has expanded to cover more use cases I have encountered, but I would love to make it cover more!



## Citing `tidybayes`



Matthew Kay (`r format(Sys.Date(), "%Y")`). _tidybayes: Tidy Data and Geoms for Bayesian Models_. R package version `r getNamespaceVersion("tidybayes")`, .

DOI: [10.5281/zenodo.1308151](https://doi.org/10.5281/zenodo.1308151).