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 be binary, and (5) analyses can incorporate the effects of reliability and skew, both of the interacting variables, as well as of the outcome variable.

**For more information see documentation and examples, and the FAQ.**

We also have a website app which implements the major functions, available **here**.

Please report bugs, issues, or questions as an Issue on Github.

You can install InteractionPoweR from github with:

```
install.packages("devtools")
devtools::install_github("dbaranger/InteractionPoweR")
```

Sometimes there will be a minor installation error, which can be resolved by using:

```
install.packages("devtools")
devtools::install_github("dbaranger/InteractionPoweR/@HEAD")
```

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. **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`

).

```
library(InteractionPoweR)
library(tictoc)
tic()
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
seed = 581827 # seed, for reproducibility - this generally should not be set
)
#> [1] "Checking for errors in inputs..."
#> [1] "Performing 1000 simulations"
toc()
#> 18.19 sec elapsed
test_power
#> # A tibble: 1 x 1
#> pwr
#> <dbl>
#> 1 0.809
```

We see that we have 80.9% power to detect the effect of interest.