In this third post we explore different ways to fit a model with a mean-centered predictor, what this means for how we set our priors with brms, and what this all means for the underlying Stan code. Along the way we practice with the transformed data block, and get fancy with the model matrix.
In the first post of this series, we focused on a single-level intercept-only model. In this post we expand the model to include a single continuous predictor. Along the way, we review matrix notation, model matrices, and a new class of distribution functions for Stan.
In this series, we’ll learn how to fit models with rstan, by parsing the Stan code underlying models fit with brms. In this first post, we start with a simple intercept-only Gaussian model.