mdc: Martingale Difference Correlation (MDC)

In MDCcure: Martingale Dependence Tools and Testing for Mixture Cure Models

Martingale Difference Correlation (MDC)

Description

mdc computes the squared martingale difference correlation between a response variable Y and explanatory variable(s) X, measuring conditional mean dependence. X can be either univariate or multivariate.

Usage

mdc(X, Y, center = "U")

Arguments

X	A vector or matrix where rows represent samples and columns represent variables.
Y	A vector or matrix where rows represent samples and columns represent variables.
center	Character string indicating the centering method to use. One of: "U": U-centering, which provides an unbiased estimator. "D": Double-centering, which leads to a biased estimator.

Value

Returns the squared martingale difference correlation of Y given X.

References

Shao, X., and Zhang, J. (2014). Martingale difference correlation and its use in high-dimensional variable screening. Journal of the American Statistical Association, 109(507), 1302-1318. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2014.887012")}.

See Also

mdd, mdc_test

Examples

# Generate example data
set.seed(123)
n <- 50
x <- matrix(rnorm(n * 5), nrow = n)  # multivariate data with 5 variables
y <- rbinom(n, 1, 0.5)               # binary covariate

# Compute MDC with U-centering
mdc(x, y, center = "U")

# Compute MDC with double-centering
mdc(x, y, center = "D")

MDCcure documentation built on Aug. 8, 2025, 6:10 p.m.