Conditional logistic models with brms: Rough draft.

After tremendous help from Henrik Singmann and Mattan Ben-Shachar, I finally have two (!) workflows for conditional logistic models with brms. These workflows are on track to make it into the next update of my ebook translation of Kruschke’s text. But these models are new to me and I’m not entirely confident I’ve walked them out properly. The goal of this blog post is to present a draft of my workflow, which will eventually make it’s way into Chapter 22 of the ebook.

Multilevel models and the index-variable approach

PhD candidate Huaiyu Liu recently reached out with a question about how to analyze clustered data. Liu’s basic setup was an experiment with four conditions. The dependent variable was binary, where success = 1, fail = 0. Each participant completed multiple trials under each of the four conditions. The catch was Liu wanted to model those four conditions with a multilevel model using the index-variable approach McElreath advocated for in the second edition of his text. Like any good question, this one got my gears turning. Thanks, Liu! The purpose of this post will be to show how to model data like this two different ways.

Make model diagrams, Kruschke style

You too can make Kruschke-style model diagrams with the tidyverse and patchwork packages. Here’s how.

Make rotated Gaussians, Kruschke style

You too can make sideways Gaussian density curves within the tidyverse. Here’s how.