int3ract: Johnson-Neyman Technique and its Three-Way Extension for Frequentist and Bayesian Models in R

TL;DR

The paper introduces the int3ract R package that implements Johnson-Neyman techniques for visualizing and interpreting interaction effects in both frequentist and Bayesian models, including three-way interactions.

Contribution

It provides a versatile R package that automates Johnson-Neyman analysis for various models and extends it to three-way interactions with visualizations.

Findings

Supports models fitted with lm(), glm(), siena(), lmer(), glmer()

Produces visual plots highlighting significant interaction regions

Enables analysis of Bayesian models with posterior distributions

Abstract

Interaction effects are ubiquitous in applied statistical modelling, yet their meaningful interpretation remains challenging. The classic Johnson-Neyman (JN) technique (Johnson and Neyman 1936) addresses this challenge for two-way interactions by identifying the regions of a moderator's range over which a focal effect is and is not statistically significant. The int3ract package for R implements the JN technique and its three-way extension (the Johnson-Neyman-Krause, or JNK, technique) for both frequentist and Bayesian models. The function JNK_freq() auto-detects models fitted via lm()/glm(), RSiena's siena(), or lme4's lmer()/glmer(), but can also be applied to multiplicative interactions from (virtually) any model family by supplying a coefficient vector and covariance matrix directly. For Bayesian Stochastic Actor-Oriented Models (SAOMs) estimated with multiSiena, or any model producing posterior draws, the function JNK_bayes() produces conditional posterior distributions. For two-way interactions, classic shaded confidence-band plots are created that visually demarcate significant and non-significant regions along the moderator range; three-way interactions yield colour-gradient heatmaps with optional crosshatch overlays for non-significant regions. The package is designed to encourage richer, region-specific reporting of interaction effects in place of the conventional single-slope spotlight approach.