Revision fe07bfa906d7e155439160caee538a3449cd3877 authored by Dominique Makowski on 08 April 2019, 08:42:41 UTC, committed by cran-robot on 08 April 2019, 08:42:41 UTC
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example1_GLM.Rmd
---
title: "Example 1: Bayesian (General) Linear Models"
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 1: Bayesian (General) Linear Models}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
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 -->
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