Setting Up R for Bayesian Analysis: A Comprehensive Guide
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Chapter 1: Introduction to Bayesian Analysis in R
If you were in the United States today, I hope you got to witness the eclipse. As someone who enjoys such celestial events, I found myself outdoors for longer than I intended, leaving me with some catching up to do on my work. Fortunately, today’s post will be straightforward for those who are familiar with R.
Previously, we discussed how Bayesian methods integrate prior knowledge into statistical modeling.
Getting Started with R
This section outlines a brief guide I utilize to help newcomers get acquainted with R and R Studio for statistical analysis. R is a powerful tool for various statistical tasks, including Bayesian analysis, and excels in data visualization.
Setting Up the Bayesian Environment in R
We will delve into several packages, but let’s start with the process of loading and installing two key packages that we will use in upcoming posts.
To install and load the necessary packages, use the following commands:
install.packages(c("brms", "bayesplot"))
library(brms)
library(bayesplot)
What Each Package Does
- brms: This package allows for fitting Bayesian regression models using a formula syntax that's easy to grasp in R.
- bayesplot: This package provides tools to visually summarize Bayesian models and their posterior distributions.
Getting to Know brms
Using brms, you can fit a Bayesian linear regression model with the following code:
library(brms)
fit <- brm(mpg ~ wt, data = mtcars, family = gaussian())
print(fit)
Visualizing Results with bayesplot
To visualize the posterior distributions of model parameters, you can use bayesplot as follows:
library(bayesplot)
mcmc_areas(as.array(fit), pars = c("Intercept", "wt"))
Chapter 2: Resources for R and Bayesian Analysis
For those looking to enhance their understanding of R and setting it up for Bayesian analysis, here are some valuable resources:
For further readings, consider these books and lectures:
The first video provides a fundamental overview of Bayesian estimation using R, making it an excellent starting point for beginners.
The second video features Benjamin Goodrich, who introduces Bayesian computation utilizing the rstanarm R package, offering deeper insights into the process.