---
title: "Chapter 13: Bayesian inference and decision making with bayestestR"
author: "Kjell Nygren"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Chapter 13: Bayesian inference and decision making with bayestestR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE
)
```

```{r setup}
library(glmbayes)
library(bayestestR)
```

## 1. Purpose

[**bayestestR**](https://easystats.github.io/bayestestR/) implements a wide range **indices** for posterior description and decision-oriented summaries (probability of direction, ropes, equivalence tests, etc.). Here we treat the **`coefficients`** component of a **`glmb`** fit as stored **i.i.d.** draws and summarize them using **bayestestR**.

This complements **Chapter 12** (optional **bayesplot** graphics, commented out in the vignette) with tabular summaries you can cite in applied reports.

## 2. Example: logistic regression posterior for coefficients

```{r menarche-fit}
data(menarche, package = "MASS")

Age2 <- menarche$Age - 13
Menarche_Model_Data <- data.frame(
  Menarche  = menarche$Menarche,
  Total     = menarche$Total,
  Age2      = Age2
)

ps <- Prior_Setup(
  cbind(Menarche, Total - Menarche) ~ Age2,
  family = binomial(link = "logit"),
  data   = Menarche_Model_Data
)

fit_logit <- glmb(
  cbind(Menarche, Total - Menarche) ~ Age2,
  family  = binomial(link = "logit"),
  pfamily = dNormal(mu = ps$mu, Sigma = ps$Sigma),
  data    = Menarche_Model_Data,
  n             = 800,
  use_parallel  = FALSE
)

coef_draws <- as.data.frame(fit_logit$coefficients)
```

## 3. Point and interval summaries

```{r describe}
bayestestR::describe_posterior(coef_draws)
bayestestR::hdi(coef_draws, ci = 0.89)
bayestestR::rope(coef_draws, range = c(-0.02, 0.02))
bayestestR::p_direction(coef_draws)
```

Tune **`rope(..., range = ...)`** to match substantive “practical equivalence” hypotheses on the **logit** scale (see also the binomial likelihood discussion in **Chapter 09**). For richer reporting pipelines (**parameters**, **performance**, …), see the **easystats** ecosystem linking out from **`?bayestestR`**.

## See also

- **Chapter 12** — optional **`bayesplot`** workflows and **`legacy_code/pp_check.glmb.R`**.  
- **Chapter 08** — prior-to-posterior workflow and **`summary.glmb()`** defaults.