Centering several variables

One of the most common issues in regression analyses of even balanced experimental data is collinearity between main effects and interactions. To avoid this problem, a simple first step is to center all predictors. In my experience folks often fail to do that simply because it’s a bit more work and we’re all lazy. So here’s an attempt at a simple R function that takes single variables as well as entire dataframes.

One slide on developing a regression model with interpretable coefficients

While Victor Kuperman and I are preparing our slides for WOMM, I’ve been thinking about how to visualize the process from input variables to a full model. Even though it involves many steps that hugely depend on the type of regression model, which in turn depends on the type of outcome (dependent) variable, there are a number of steps that one always needs to go through if we want interpretable coefficient estimates (as well as unbiased standard error estimates for those coefficients).