Compute congruence coefficient

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

Usage

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?

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

See also

cor

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples

vec1 <- runif(1000)
vec2 <- runif(1000)

CC(vec1, vec2)
#> [1] 0.7539623
# compare to cor()
cor(vec1, vec2)
#> [1] 0.009919872

# column input
df <- data.frame(vec1, vec2, vec3 = runif(1000))
CC(df)
#>           vec1      vec2      vec3
#> vec1 1.0000000 0.7539623 0.7528033
#> vec2 0.7539623 1.0000000 0.7574734
#> vec3 0.7528033 0.7574734 1.0000000
cor(df)
#>             vec1        vec2       vec3
#> vec1 1.000000000 0.009919872 0.02368507
#> vec2 0.009919872 1.000000000 0.02560829
#> vec3 0.023685075 0.025608293 1.00000000