cmdm_test {EDMeasure}  R Documentation 
cmdm_test
tests conditional mean independence of Y
given X
conditioning on Z
,
where each contains one variable (univariate) or more variables (multivariate).
All tests are implemented as permutation tests.
cmdm_test(X, Y, Z, num_perm = 500, type = "linmdd", 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. 
Z 
A vector, matrix or data frame, where rows represent samples, and columns represent variables. 
num_perm 
The number of permutation samples drawn to approximate the asymptotic distributions of mutual dependence measures. 
type 
The type of conditional mean dependence measures, including

compute 
The computation method for martingale difference divergence, including

center 
The centering approach for martingale difference divergence, including

cmdm_test
returns a list including the following components:
stat 
The value of the conditional mean dependence measure. 
dist 
The pvalue of the conditional mean independence test. 
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.
## Not run: # X, Y, Z are vectors with 10 samples and 1 variable X < rnorm(10) Y < rnorm(10) Z < rnorm(10) cmdm_test(X, Y, Z, type = "linmdd") # 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) cmdm_test(X, Y, Z, type = "pmdd") ## End(Not run)