coverage_correlation

Function coverage_correlation, implements the coverage correlation coefficient introduced in the paper Coverage correlation: detecting singular dependencies between random variables. The coverage correlation coefficient, is a nonparametric measure of statistical association designed to detect dependencies concentrated on low-dimensional structures within the joint distribution of two random variables or vectors. Based on Monge–Kantorovich ranks and geometric coverage processes, this statistic quantifies the extent to which the joint distribution concentrates on a singular subset with respect to the product of the marginals. The coverage correlation coefficient is distribution-free, admits an analytically tractable asymptotic null distribution, and can be computed efficiently, making it well-suited for uncovering complex, potentially nonlinear associations in large-scale pairwise testing.

library(covercorr)

Example 1

In this example, we demonstrate how to usecoverage_correlation with a simple simulation. We compute the coverage correlation coefficient between two one dimensional Normal random variables, X and Y, and then vary the strength of their relationship to observe how the statistic changes.

n <- 100
p <- 1
X <- rnorm(n)
Y <- rnorm(n)
result <- coverage_correlation(Y, X, visualise = TRUE)

str(result)
#> List of 4
#>  $ stat  : num 0.0139
#>  $ pval  : num 0.323
#>  $ method: chr "exact"
#>  $ mc_se : num 0

In the example above, X and Y are independent.
The parameter visualise defaults to FALSE, but setting it to TRUE produces a plot that illustrates the intuition behind the coverage correlation coefficient.

The coverage correlation coefficient first transforms X and Y into their Monge–Kantorovich ranks, denoted by X_rank and Y_rank, which are uniformly distributed on \([0, 1]\). The plot displays the pairs \((X_{\text{rank}_i}, Y_{\text{rank}_i})\) along with cubes of volume \(n^{-1}\).

Inside the function coverage_correlation, we compute \(V_n\), the total uncovered area after taking the union of these cubes. The coverage correlation coefficient is then defined as

\[ \kappa_n^{X, Y} := \frac{V_n - e^{-1}}{1 - e^{-1}}. \]

The function returns a list with four elements:

By default, method = "auto". In this mode, if the total dimension of X and Y
(i.e., ncol(X) + ncol(Y), treating vectors as one-dimensional) is at most 6,
the method is set to "exact"; otherwise, it uses "approx".

Next we can see how the result changes as we introduces dependence between X and Y

n <- 100
p <- 1
X <- rnorm(n)
Z <- rnorm(n)
rho <- 0.9
Y <- rho * X + sqrt(1 - rho^2) * Z
result <- coverage_correlation(Y, X, visualise = TRUE)

str(result)
#> List of 4
#>  $ stat  : num 0.264
#>  $ pval  : num 0
#>  $ method: chr "exact"
#>  $ mc_se : num 0

You may notice parts of some cubes appearing at the corners of the plot.
This happens because we treat \([0, 1]^2\) as a torus.
If a cube centered at one of the rank points lies partially outside
\([0, 1]^2\), we wrap it around so that the plot reflects this topology.

Example 2

The coverage correlation coefficient can handle multidimensional random vectors as well.

n <- 100
p <- 2
X <- matrix(rnorm(p * n), ncol = p)
Y <- matrix(0, nrow = n, ncol = p)
Y[, 1] <- X[, 1]^2
Y[, 2] <- X[, 1] * X[, 2]
result <- coverage_correlation(Y, X)
str(result)
#> List of 4
#>  $ stat  : num 0.278
#>  $ pval  : num 0
#>  $ method: chr "exact"
#>  $ mc_se : num 0

In this case we cannot visualise the whole plot as X and Y are not one-dimensional.

Example 3

In the example below, X and Y are independent and 2-dimensional. We set the method parameter equal to approx.

n <- 50
p <- 2
X <- matrix(rnorm(p * n), ncol = p)
Y <- matrix(rnorm(p * n), ncol = p)
result <- coverage_correlation(Y, X, method = 'approx')
str(result)
#> List of 4
#>  $ stat  : num -0.0144
#>  $ pval  : num 0.708
#>  $ method: chr "approx"
#>  $ mc_se : num 0.0255