brms

Notes on the Bayesian cumulative probit

What/why? Prompted by a couple of my research projects, I’ve been fitting a lot of ordinal models, lately. Because of its nice interpretive properties, I’m fond of using the cumulative probit.

Use emmeans() to include 95% CIs around your lme4-based fitted lines

Scenario You’re an R (R Core Team, 2020) user and just fit a nice multilevel model to some grouped data and you’d like to showcase the results in a plot.

Conditional logistic models with brms: Rough draft.

Preamble 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 (see here).

One-step Bayesian imputation when you have dropout in your RCT

Preamble Suppose you’ve got data from a randomized controlled trial (RCT) where participants received either treatment or control. Further suppose you only collected data at two time points, pre- and post-treatment.

Got overdispersion? Try observation-level random effects with the Poisson-lognormal mixture

What? One of Tristan Mahr’s recent Twitter threads almost broke my brain. wait when people talk about treating overdispersion by using random effects, they sometimes put a random intercept on each row?

Make ICC plots for your brms IRT models

Context Someone recently posted a thread on the Stan forums asking how one might make item-characteristic curve (ICC) and item-information curve (IIC) plots for an item-response theory (IRT) model fit with brms.

Don't forget your inits

tl;dr When your MCMC chains look a mess, you might have to manually set your initial values. If you’re a fancy pants, you can use a custom function.

Effect sizes for experimental trials analyzed with multilevel growth models: Two of two

Orientation This post is the second and final installment of a two-part series. In the first post, we explored how one might compute an effect size for two-group experimental data with only \(2\) time points.

Regression models for 2-timepoint non-experimental data

Purpose In the contemporary longitudinal data analysis literature, 2-timepoint data (a.k.a. pre/post data) get a bad wrap. Singer and Willett (2003, p. 10) described 2-timepoint data as only “marginally better” than cross-sectional data and Rogosa et al.

Multilevel models and the index-variable approach

The set-up 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.