pmdc 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.
Usage
pmdc(X, Y, Z)
Arguments
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.
Value
pmdc returns the squared partial martingale difference correlation
of Y given X adjusting for the dependence on Z.
References
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.
# NOT RUN {# X, Y, Z are 10 x 2 matrices with 10 samples and 2 variablesX <- matrix(rnorm(10 * 2), 10, 2)
Y <- matrix(rnorm(10 * 2), 10, 2)
Z <- matrix(rnorm(10 * 2), 10, 2)
pmdc(X, Y, Z)
# }