As a data analyst, there’s nothing more exciting than uncovering a significant interaction in a logistic regression model. But, let’s be real, interpreting those interactions can be a daunting task. I’ve been there too, staring at my screen, wondering what those numbers really mean. In this post, I’ll share my journey of trying to make sense of a significant interaction, and hopefully, you’ll find some helpful takeaways.
The Problem
I was working on a logistic regression mixed-effects model, and I stumbled upon a significant interaction between two factors, F1 and F2. The dependent variable was binomial, and I had specified contrast codes for F2. The interaction was significant, but I was stuck on how to interpret it.
I tried two approaches, but I wasn’t sure which one was correct. I created two separate datasets, one for each level of F1, and fit a new model for each. Then, I exponentiated the estimated term to determine the odds ratio. But, I couldn’t find any support for this approach, and I was unclear whether I should include the random effects or not.
The Phia Package
Online searches recommended using the ‘phia’ package and the ‘testInteractions’ function. But, the output gave me only a single value for the desired contrast, and I didn’t know how to interpret it or what units it was in.
The Solution
After some digging, I realized that the ‘phia’ package is a powerful tool for interpreting interactions, but it requires some understanding of how it works. The ‘testInteractions’ function provides a simple way to test the interaction, but it’s essential to understand the underlying assumptions and limitations.
To interpret the interaction, I needed to consider the contrast codes I had specified for F2. The interaction term represented the difference in the effect of F2 between the two levels of F1. By using the ‘phia’ package, I could estimate the odds ratio for each level of F1, and then compare them to understand the interaction.
Takeaways
Interpreting significant interactions in logistic regression models requires careful consideration of the model specifications, contrast codes, and the assumptions of the analysis. The ‘phia’ package is a valuable tool for simplifying the process, but it’s essential to understand its limitations and how to apply it correctly.
If you’re struggling to interpret a significant interaction, take a step back, and revisit your model specifications. Consider the contrast codes, and think about how the interaction term is estimated. With patience and practice, you’ll become more confident in your ability to unravel the mystery of significant interactions.
*Further reading: phia package documentation*