https://github.com/bearloga/bcda
This R package is a set of tools for Bayesian analysis of categorical data, specifically 2×2 contingency tables.
https://github.com/bearloga/bcda
bayesian-data-analysis bayesian-inference r r-package rstats
Last synced: 10 months ago
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This R package is a set of tools for Bayesian analysis of categorical data, specifically 2×2 contingency tables.
- Host: GitHub
- URL: https://github.com/bearloga/bcda
- Owner: bearloga
- License: mit
- Created: 2016-04-05T19:13:05.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2021-02-04T15:49:27.000Z (about 5 years ago)
- Last Synced: 2025-03-24T13:44:13.539Z (11 months ago)
- Topics: bayesian-data-analysis, bayesian-inference, r, r-package, rstats
- Language: R
- Size: 342 KB
- Stars: 6
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
title: Bayesian Categorical Data Analysis
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
options(digits = 2)
set.seed(0)
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 12))
```
# BCDA
This is a set of tools for Bayesian analysis of categorical data, specifically 2×2 contingency tables.
Use:
- `beta_binom()` for analysis using the Beta-Binomial model
- `print()`, `tidy()`, `glance()`, and `plot()` to view the results
- `present_bbfit()` if you want a nicely formatted table of summaries to include in a presentation or report
- `update()` if you have additional data
- `est_multinom()` for estimating multinomial cell probabilities
## Installation
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("remotes")
remotes::install_github("bearloga/BCDA")
```
## Example
`beta_binom()` works with a table of the following format:
| | Success | Failure |
|--------:|:-------:|:-------:|
| Group 1 | n11 | n12 |
| Group 2 | n21 | n22 |
and uses the Beta-Binomial model to estimate:
| | Success | Failure |
|--------:|:-------:|:-------:|
| Group 1 | p1 | 1 - p1 |
| Group 2 | p2 | 1 - p2 |
which allows inference on the difference (`p1 - p2`), the risk ratio (RR), and the odds ratio (OR), defined as follows:
- `RR = p1 / p2` -- how much more likely success in group 1 is, relative to success in group 2
- `OR = (p1/(1-p1)) / (p2/(1-p2))` -- the ratio of the odds -- where `p1/(1-p1)` is the odds of success in group 1 and `p2/(1-p2)` is the odds of success in group 2
All examples will use the following fake data:
```{r fake-data}
fake_data <- matrix(c(200, 150, 250, 300), nrow = 2, byrow = TRUE)
colnames(fake_data) <- c('Safe' ,'Dangerous')
rownames(fake_data) <- c('Animals', 'Plants')
```
```{r, echo=FALSE}
knitr::kable(fake_data)
```
**Note** that `beta_binom()` uses the Jeffreys prior by default and that this README was knit with the seed set to 0 at the start for reproducibility.
```{r example-initial}
library(BCDA)
(fit <- beta_binom(fake_data))
```
The credible intervals above are calculated using quantiles. If we have the **coda** package installed, we can also obtain the high posterior density intervals:
```{r highest-posterior-density}
print(fit, interval_type = "HPD")
```
```{r plotting-params, fig.height=4, fig.width=8, dpi=72, fig.cap='Preview of visualization of the posterior draws.', fig.path='figures/'}
plot(fit)
```
## Presentation of the results
The package includes a variety of functions for looking at the results from fitting a `beta_binom()` model. To aid in functional programming, we implemented the `tidy()` and `glance()` verbs:
```{r tidy-output, results='asis'}
library(magrittr) # for %>%
fit %>%
tidy %>%
knitr::kable()
```
When knitting R Markdown to HTML, you can use the [{gt}](https://gt.rstudio.com/) package for creating tables:
```{r tidy-output-gt, eval=FALSE}
library(gt)
fit %>%
tidy %>%
gt(rowname_col = "term") %>%
tab_header(md("Results of `beta_binom()`"))
```
`glance()` is used to
> Construct a single row summary "glance" of a model, fit, or other object
```{r glance_output}
fit %>%
glance
```
This is perfectly okay in an interactive data analysis scenario, but not when presenting the results in a report. `glance()` is actually a special case of the `present_bbfit()` function which generates all those nicely formatted credible intervals but outputs a Markdown/LaTeX-formatted table by default:
```{r, results='asis'}
present_bbfit(fit)
```
The point estimates include credible intervals by default but these can be turned off:
```{r, results='asis'}
present_bbfit(fit, conf_interval = FALSE, digits = 3)
```
Since the underlying code uses `tidy()` to compute the summaries, we can specify a particular credible level and the type of interval we want (e.g. highest posterior density):
```{r, results='asis'}
present_bbfit(fit, conf_level = 0.89, interval_type = "HPD")
```
It also supports multiple models, which can be provided as a named or an unnamed list. See the example below.
## Updating the posterior
In Bayesian statistics, we can reuse a previously computed posterior as a prior if we have additional data, allowing us to update the parameter estimates as new data becomes available. Suppose we collect 40 observations from 2 groups (20 per group) on the first day of the A/B test, and 10 observations per day for the next 2 weeks. Here we see what happens when we update the posterior with additional data on a daily basis:
### Example Code
```{r updating_posterior, results='asis'}
fit_2 <- update(fit, x = c(100, 200), n = c(400, 600))
present_bbfit(list("Day 1" = fit, "Day 2" = fit_2))
```
## See also
Other packages for Bayesian analysis of A/B tests include: [LearnBayes](https://cran.r-project.org/web/packages/LearnBayes/index.html) (GPL), [conting](https://cran.r-project.org/web/packages/conting/index.html) (GPL), [bandit](https://cran.r-project.org/web/packages/bandit/index.html) (GPL), [testr](https://github.com/ayakubovich/testr) (MIT).
---------
Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.