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.

If you fit a model with multiply imputed data, you can still plot the line.

If you’re an R user and like multiple imputation for missing data, you probably know all about the mice package. The bummer is there are no built-in ways to plot the fitted lines from models fit from multiply-imputed data using van Buuren’s mice-oriented workflow. However, there is a way to plot your fitted lines by hand and in this blog post I’ll show you how.

Sexy up your logistic regression model with logit dotplots

The major shortcoming in typical logistic regression line plots is they usually don’t show the data due to overplottong across the y-axis. Happily, new developments with Matthew Kay’s ggdist package make it easy to show your data when you plot your logistic regression curves. In this post I’ll show you how.

Make ICC plots for your brms IRT models

The purpose of this blog post is to show how one might make ICC and IIC plots for brms IRT models using general-purpose data wrangling steps.

Make model diagrams, Kruschke style

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

Would you like all your posteriors in one plot?

In response to a DM question, here we practice a few different ways you can combine the posterior samples from your Bayesian models into a single plot.

Make rotated Gaussians, Kruschke style

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