NNS.copula {NNS} | R Documentation |
Determines higher dimension dependence coefficients based on co-partial moment matrices ratios.
NNS.copula( X, target = NULL, continuous = TRUE, plot = FALSE, independence.overlay = FALSE, ncores = NULL )
X |
a numeric matrix or data frame. |
target |
numeric; Typically the mean of Variable X for classical statistics equivalences, but does not have to be. (Vectorized) |
continuous |
logical; |
plot |
logical; |
independence.overlay |
logical; |
ncores |
integer; value specifying the number of cores to be used in the parallelized procedure. If NULL (default), the number of cores to be used is equal to the number of cores of the machine - 1. |
Returns a multivariate dependence value [0,1].
Fred Viole, OVVO Financial Systems
Viole, F. (2016) "Beyond Correlation: Using the Elements of Variance for Conditional Means and Probabilities" https://www.ssrn.com/abstract=2745308.
set.seed(123) x <- rnorm(1000) ; y <- rnorm(1000) ; z <- rnorm(1000) A <- data.frame(x, y, z) NNS.copula(A, plot = TRUE, independence.overlay = TRUE, ncores = 1) ### Target 0 NNS.copula(A, target = rep(0, ncol(A)), plot = TRUE, independence.overlay = TRUE, ncores = 1)