Raw File
example2.Rmd
---
title: "Example 2: Confirmation of Bayesian skills"
output: 
  github_document:
    toc: true
    fig_width: 10.08
    fig_height: 6
  rmarkdown::html_vignette:
    toc: true
    fig_width: 10.08
    fig_height: 6
tags: [r, bayesian, posterior, test]
vignette: >
  \usepackage[utf8]{inputenc}
  %\VignetteIndexEntry{Example 2: Confirmation of Bayesian skills}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
bibliography: bibliography.bib
---

This vignette can be referred to by citing the package:

- Makowski, D., Ben-Shachar M. S. \& L├╝decke, D. (2019). *Understand and Describe Bayesian Models and Posterior Distributions using bayestestR*. Available from https://github.com/easystats/bayestestR. DOI: [10.5281/zenodo.2556486](https://zenodo.org/record/2556486).

---

```{r message=FALSE, warning=FALSE, include=FALSE}
library(knitr)
options(knitr.kable.NA = '')
knitr::opts_chunk$set(comment=">")
options(digits=2)

set.seed(333)
```


Now that [**describing and understanding posterior distributions of linear regressions**](https://easystats.github.io/bayestestR/articles/example1.html) has no secrets for you, let's go deeper.

> **"But it's only about regressions and I want to do ANOVAs and t-tests"**

Don't worry. Let us all remind and appreciate the fact that **all basic statistical pocedures** such as t-tests, ANOVAs, correlations or Chisquare tests [***are* linear regressions**](https://lindeloev.github.io/tests-as-linear/). Now that we have accepted the beauty of it, we will continue with a general linear model.


## Logistic Model



### Make some figures

Visualization.

### Diagnostic Indices

About diagnostic indices such as Rhat and ESS.


## Mixed Model


### Priors

About priors.
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