The InteractionPoweR package conducts power analyses for regression models in cross-sectional data sets where the term of interest is an interaction between two variables, also known as ‘moderation’ analyses. The package includes functions for simulating data sets, conducting power analyses, and plotting and interpreting results. Notable package features include (1) the ability to compute power for interactions between two continuous variables, (2) effect sizes are all specified as the cross-sectional Pearson’s correlation, (3) simulations do not assume that the interacting variables are independent, (4) any variable in the model, including the outcome, can have anywhere from 2 (i.e., binary) to 20 discrete values, and (5) analyses can incorporate the effects of reliability and skew, both of the interacting variables, as well as of the outcome variable.
We also have a Shiny app which implements the major functions in a user-friendly point-and-click interface.
Please report bugs, issues, or questions as an Issue on Github.
You can install InteractionPoweR from github with:
Sometimes there will be a minor installation error, which can be resolved by using:
If you get an error about a corrupt .rdb file, try restarting your R session.
The simplest use-case is when all the input parameters are known. We know the population-level correlation between our predictors (x1 and x2) and our outcome, we have a smallest effect size of interest in mind for our interaction effect size, and our sample size is already set (maybe we are conducting secondary data analysis). Power can be determined with a single command.
First - analytic power, using variable correlations (and reliability, if provided) to estimate how much additional variance is explained by the interaction term.
library(InteractionPoweR) test_power<-power_interaction_r2( alpha = 0.05, # alpha, for the power analysis N = 350, # sample size r.x1x2.y = .15, # interaction effect to test (correlation between x1*x2 and y) r.x1.y = .2, # correlation between x1 and y r.x2.y = .1, # correlation between x2 and y r.x1.x2 = .2 # correlation between x1 and x2 ) #>  "Checking for errors in inputs..." test_power #> pwr #> 1 0.8055776
We see that we have 80% power.
We can also use simulations to estimate power. Simulations are particularly useful because they can account for non-normal data, including variable skew, binary variables, and likert variables.
NB In all these examples we use 1000 simulations for speed (
n.iter = 1000), but for robust results we recommend 10,000 simulations (
n.iter = 10000).
test_power<-power_interaction( n.iter = 1000, # number of simulations per unique combination of input parameters alpha = 0.05, # alpha, for the power analysis N = 350, # sample size r.x1x2.y = .15, # interaction effect to test (correlation between x1*x2 and y) r.x1.y = .2, # correlation between x1 and y r.x2.y = .1, # correlation between x2 and y r.x1.x2 = .2, # correlation between x1 and x2 k.y = 2, # y is binary (has 2 levels) - analyses are run as logistic regressions k.x1 = 5, # x1 has 5 levels (is a likert variable) skew.x1 = .5, # x1 has a skew of 0.5 skew.x2 = 1.5, # x2 is a continuous variable and has a skew of 1.5 seed = 581827 # seed, for reproducibility ) #>  "Checking for errors in inputs..." #>  "Adjusting correlations for variable transformations..." #>  "Performing 1000 simulations" test_power #> N pwr #> 1 350 0.75
The simulation estimates 75% power - it’s accuracy will increase with more iterations.
Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM (2022). “Tutorial: Power analyses for interaction effects in cross-sectional regressions.” PsyArxiv. doi: 10.31234/osf.io/5ptd7