---
title: "Example 1: Bayesian (General) Linear Models"
output: 
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tags: [r, bayesian, posterior, test]
vignette: >
  \usepackage[utf8]{inputenc}
  %\VignetteIndexEntry{Example 1: Bayesian (General) Linear Models}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
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bibliography: bibliography.bib
---



Now that you've read the [**Get started**](https://easystats.github.io/bayestestR/articles/bayestestR.html) section, let's dive in the subtleties of Bayesian modelling using R.

## Loading the packages

Once you've [installed](https://easystats.github.io/bayestestR/articles/bayestestR.html) the necessary packages, we can load `rstanarm` (to fit the models) and `bayestestR` (to compute useful indices).

```{r message=FALSE, warning=FALSE}
library(rstanarm)
library(bayestestR)
```

## A Linear Model

### Fitting the model


```{r message=FALSE, warning=FALSE, eval=FALSE}
model <- stan_glm(Sepal.Length ~ Petal.Length, data=iris)
```
```{r echo=FALSE, message=FALSE, warning=FALSE, comment=NA, results='hide'}
library(rstanarm)
set.seed(333)

model <- stan_glm(Sepal.Length ~ Petal.Length, data=iris)
```

### Extracting the posterior


```{r message=FALSE, warning=FALSE, eval=FALSE}
posteriors <- insight::get_parameters(model)
```
```{r message=FALSE, warning=FALSE, eval=FALSE}
plot(density(posteriors$Petal.Length))
```


<!-- ### Correspondance with Frequentist Models -->

<!-- TO BE IMPROVED -->