mdm measures mutual dependence of all components in X, where each component contains one variable (univariate) or more variables (multivariate).
mdm(X, dim_comp =NULL, dist_comp =FALSE, type ="comp_simp")
Arguments
X: A matrix or data frame, where rows represent samples, and columns represent variables.
dim_comp: The numbers of variables contained by all components in X. If omitted, each component is assumed to contain exactly one variable.
dist_comp: Logical. If TRUE, the distances between all components from all samples in X will be returned.
type: The type of mutual dependence measures, including
asym_dcov: asymmetric measure Rn based on distance covariance Vn;
sym_dcov: symmetric measure Sn based on distance covariance Vn;
comp: complete measure Qn based on complete V-statistics;
comp_simp: simplified complete measure Qn⋆ based on incomplete V-statistics;
asym_comp: asymmetric measure Jn based on complete measure Qn;
asym_comp_simp: simplified asymmetric measure Jn⋆ based on simplified complete measure Qn⋆;
sym_comp: symmetric measure In based on complete measure Qn;
sym_comp_simp: simplified symmetric measure In⋆ based on simplified complete measure Qn⋆.
From experiments, asym_dcov, sym_dcov, comp_simp are recommended.
Returns
mdm returns a list including the following components: - stat: The value of the mutual dependence measure.
dist: The distances between all components from all samples.
Examples
# X is a 10 x 3 matrix with 10 samples and 3 variablesX <- matrix(rnorm(10*3),10,3)# assume X = (X1, X2) where X1 is 1-dim, X2 is 2-dimmdm(X, dim_comp = c(1,2), type ="asym_dcov")# assume X = (X1, X2) where X1 is 2-dim, X2 is 1-dimmdm(X, dim_comp = c(2,1), type ="sym_dcov")# assume X = (X1, X2, X3) where X1 is 1-dim, X2 is 1-dim, X3 is 1-dimmdm(X, dim_comp = c(1,1,1), type ="comp_simp")
References
Jin, Z., and Matteson, D. S. (2017). Generalizing Distance Covariance to Measure and Test Multivariate Mutual Dependence. arXiv preprint arXiv:1709.02532. https://arxiv.org/abs/1709.02532.