pmdd: Partial Martingale Difference Divergence

In zejin/CMDMeasure: Conditional Mean Dependence Measures via Energy Statistics

Description

pmdd measures conditional mean dependence of Y given X conditioning on Z, where each contains one variable (univariate) or more variables (multivariate).

Usage

pmdd(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

pmdd returns the value of squared partial martingale difference divergence.

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.

Examples

# 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)

zejin/CMDMeasure documentation built on May 28, 2019, 4:42 p.m.