pmdd {EDMeasure} | R Documentation |
pmdd
measures conditional mean dependence of Y
given X
adjusting for the
dependence on Z
, where each contains one variable (univariate) or more variables (multivariate).
Only the U-centering approach is applied.
pmdd(X, Y, Z)
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. |
Z |
A vector, matrix or data frame, where rows represent samples, and columns represent variables. |
pmdd
returns the squared partial martingale difference divergence
of Y
given X
adjusting for the dependence on Z
.
Park, T., Shao, X., and Yao, S. (2015). Partial martingale difference correlation. Electronic Journal of Statistics, 9(1), 1492-1517. http://dx.doi.org/10.1214/15-EJS1047.
# X, Y, Z are vectors with 10 samples and 1 variable
X <- rnorm(10)
Y <- rnorm(10)
Z <- rnorm(10)
pmdd(X, Y, Z)
# X, Y, Z 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)
Z <- matrix(rnorm(10 * 2), 10, 2)
pmdd(X, Y, Z)