NNS.copula: NNS Co-Partial Moments Higher Dimension Dependence

In NNS: Nonlinear Nonparametric Statistics

NNS Co-Partial Moments Higher Dimension Dependence

Description

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].

Author(s)

Fred Viole, OVVO Financial Systems

References

Viole, F. (2016) "Beyond Correlation: Using the Elements of Variance for Conditional Means and Probabilities" \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.2745308")}.

Examples

## Not run: 
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)

## End(Not run)

NNS documentation built on Oct. 14, 2024, 5:09 p.m.