This post is my good-faith effort to create a simple linear model using the Bayesian framework and workflow described by Richard McElreath in his Statistical Rethinking book.1 As always - please view this post through the lens of the eager student and not the learned master. I did my best to check my work, but it’s entirely possible that something was missed. Please let me know - I won’t take it personally.
In this post I’ll be attempting to leverage the parsnip package in R to run through some straightforward predictive analytics/machine learning. Parsnip provides a flexible and consistent interface to apply common regression and classification algorithms in R. I’ll be working with the Cleveland Clinic Heart Disease dataset which contains 13 variables related to patient diagnostics and one outcome variable indicating the presence or absence of heart disease.1 The data was accessed from the UCI Machine Learning Repository in September 2019.