mdd {EDMeasure}  R Documentation 
mdd
measures conditional mean dependence of Y
given X
,
where each contains one variable (univariate) or more variables (multivariate).
mdd(X, Y, compute = "C", center = "U")
X 
A vector, matrix or data frame, where rows represent samples, and columns represent variables. 
Y 
A vector, matrix or data frame, where rows represent samples, and columns represent variables. 
compute 
The method for computation, including

center 
The approach for centering, including

mdd
returns the squared martingale difference divergence of Y
given X
.
Shao, X., and Zhang, J. (2014). Martingale difference correlation and its use in highdimensional variable screening. Journal of the American Statistical Association, 109(507), 13021318. http://dx.doi.org/10.1080/01621459.2014.887012.
Park, T., Shao, X., and Yao, S. (2015). Partial martingale difference correlation. Electronic Journal of Statistics, 9(1), 14921517. http://dx.doi.org/10.1214/15EJS1047.
# X, Y are vectors with 10 samples and 1 variable X < rnorm(10) Y < rnorm(10) mdd(X, Y, compute = "C") mdd(X, Y, compute = "R") # X, Y are 10 x 2 matrices with 10 samples and 2 variables X < matrix(rnorm(10 * 2), 10, 2) Y < matrix(rnorm(10 * 2), 10, 2) mdd(X, Y, center = "U") mdd(X, Y, center = "D")