Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.22051 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910194032705536 |
|---|---|
| author | Krause, Robert W. |
| author_facet | Krause, Robert W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22051 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | int3ract: Johnson-Neyman Technique and its Three-Way Extension for Frequentist and Bayesian Models in R Krause, Robert W. Methodology 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. |
| title | int3ract: Johnson-Neyman Technique and its Three-Way Extension for Frequentist and Bayesian Models in R |
| topic | Methodology |
| url | https://arxiv.org/abs/2604.22051 |