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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. 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>.
Getting started
Browse package contents
Package details |
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| Author | David Baranger [aut, cre] (<https://orcid.org/0000-0002-6659-357X>, davidbaranger.com), 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 | 0.2.2 |
| URL | https://dbaranger.github.io/InteractionPoweR/ https://doi.org/10.1177/25152459231187531 |
| Package repository | View on CRAN |
| Installation |
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