mddm {EDMeasure}  R Documentation 
mddm
extends martingale difference divergence from a scalar to a matrix.
It encodes the linear combinations of all univariate components in Y
that are conditionally mean independent of X
.
Only the doublecentering approach is applied.
mddm(X, Y, compute = "C")
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

mddm
returns the martingale difference divergence matrix of Y
given X
.
Lee, C. E., and Shao, X. (2017). Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Stationary Multivariate Time Series. Journal of the American Statistical Association, 114. http://dx.doi.org/10.1080/01621459.2016.1240083.
# X, Y are vectors with 10 samples and 1 variable
X < rnorm(10)
Y < rnorm(10)
mddm(X, Y, compute = "C")
mddm(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)
mddm(X, Y, compute = "C")
mddm(X, Y, compute = "R")