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InteractionPoweR: Power Analyses for Interaction Effects in Cross-Sectional Regressions
Power analysis for regression models which test the interaction of two or three independent variables on a single dependent variable. Includes options for correlated interacting variables and specifying variable reliability. Two-way interactions can include continuous, binary, or ordinal variables. Power analyses can be done either analytically or via simulation. Includes tools for simulating single data sets and visualizing power analysis results. The primary functions are power_interaction_r2() and power_interaction() for two-way interactions, and power_interaction_3way_r2() for three-way interactions. The function run_pos_power_search() provides a stability analysis for two-way interactions. Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM (2023). "Tutorial: Power analyses for interaction effects in cross-sectional regressions." <doi:10.1177/25152459231187531>. If you use the stability analyses, please cite: Castillo A, Miller JD, Vize C, Baranger DAA, Lynam DR. "When Do Interaction/Moderation Effects Stabilize in Linear Regression?"<doi:10.1177/25152459251407860>.
Getting started
Browse package contents
Package details |
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| Author | David Baranger [aut, cre], Andrew Castillo [aut], Brandon Goldstein [ctb], Megan Finsaas [ctb], Thomas Olino [ctb], Colin Vize [ctb], Don Lynam [ctb] |
| Maintainer | David Baranger <dbaranger@gmail.com> |
| License | GPL (>= 3) |
| Version | 0.2.4 |
| URL | https://dbaranger.github.io/InteractionPoweR/ https://doi.org/10.1177/25152459231187531 https://doi.org/10.1177/25152459251407860 |
| Package repository | View on CRAN |
| Installation |
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