# CC: Compute congruence coefficient In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

## Description

Computes the congruence coefficient, also known as an "unadjusted" correlation or Tucker's congruence coefficient.

## Usage

 `1` ```CC(x, y = NULL, unname = FALSE) ```

## Arguments

 `x` a vector or `data.frame`/`matrix` containing the variables to use. If a vector then the input `y` is required, otherwise the congruence coefficient is computed for all bivariate combinations `y` (optional) the second vector input to use if `x` is a vector `unname` logical; apply `unname` to the results to remove any variable names?

## Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

## References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. `The Quantitative Methods for Psychology, 16`(4), 248-280. doi: 10.20982/tqmp.16.4.p248

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. `Journal of Statistics Education, 24`(3), 136-156. doi: 10.1080/10691898.2016.1246953

`cor`
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```vec1 <- runif(1000) vec2 <- runif(1000) CC(vec1, vec2) # compare to cor() cor(vec1, vec2) # column input df <- data.frame(vec1, vec2, vec3 = runif(1000)) CC(df) cor(df) ```