# Bayesian

## 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.

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.

## Bayesian meta-analysis in brms-II

Preamble In Section 14.3 of my (2020a) translation of the first edition of McElreath’s (2015) Statistical rethinking, I included a bonus section covering Bayesian meta-analysis. For my (2020b) translation of the second edition of the text (McElreath, 2020), I’d like to include another section on the topic, but from a different perspective.

## Make model diagrams, Kruschke style

tl;dr You too can make model diagrams with the tidyverse and patchwork packages. Here’s how. Diagrams can help us understand statistical models. I’ve been working through John Kruschke’s Doing Bayesian data analysis, Second Edition: A tutorial with R, JAGS, and Stan and translating it into brms and tidyverse-style workflow.

## Time-varying covariates in longitudinal multilevel models contain state- and trait-level information: This includes binary variables, too

tl;dr When you have a time-varying covariate you’d like to add to a multilevel growth model, it’s important to break that variable into two. One part of the variable will account for within-person variation.

## Bayesian power analysis: Part III.b. What about 0/1 data?

Version 1.1.0 Edited on April 21, 2021, to fix a few code breaks and add a Reference section. Orientation In the last post, we covered how the Poisson distribution is handy for modeling count data.

## Bayesian power analysis: Part III.a. Counts are special.

Version 1.1.0 Edited on April 21, 2021, to remove the broom::tidy() portion of the workflow. Orientation So far we’ve covered Bayesian power simulations from both a null hypothesis orientation (see part I) and a parameter width perspective (see part II).