NNS Co-Partial Moments Higher Dimension Dependence

Determines higher dimension dependence coefficients based on co-partial moment matrices ratios.

Usage

NNS.copula(
  X,
  target = NULL,
  continuous = TRUE,
  plot = FALSE,
  independence.overlay = FALSE
)

Arguments

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) (target = NULL) (default) will set the target as the mean of every variable.
continuous: logical; TRUE (default) Generates a continuous measure using degree 1 PM.matrix, while discrete FALSE uses degree 0 PM.matrix.
plot: logical; FALSE (default) Generates a 3d scatter plot with regression points.
independence.overlay: logical; FALSE (default) Creates and overlays independent Co.LPM and Co.UPM regions to visually reference the difference in dependence from the data.frame of variables being analyzed. Under independence, the light green and red shaded areas would be occupied by green and red data points respectively.

Value

Returns a multivariate dependence value [0,1].

References

Viole, F. (2016) "Beyond Correlation: Using the Elements of Variance for Conditional Means and Probabilities" doi:10.2139/ssrn.2745308 .

Author

Fred Viole, OVVO Financial Systems

Examples

if (FALSE) { # \dontrun{
set.seed(123)
x <- rnorm(1000) ; y <- rnorm(1000) ; z <- rnorm(1000)
A <- data.frame(x, y, z)
NNS.copula(A, target = colMeans(A), plot = TRUE, independence.overlay = TRUE)

### Target 0
NNS.copula(A, target = rep(0, ncol(A)), plot = TRUE, independence.overlay = TRUE)
} # }