NNS.dep: NNS Dependence

In NNS: Nonlinear Nonparametric Statistics

NNS Dependence

Description

Returns the dependence and nonlinear correlation between two variables based on higher order partial moment matrices measured by frequency or area.

Usage

NNS.dep(x, y = NULL, asym = FALSE, p.value = FALSE, print.map = FALSE)

Arguments

x	a numeric vector, matrix or data frame.
y	NULL (default) or a numeric vector with compatible dimensions to x.
asym	logical; FALSE (default) Allows for asymmetrical dependencies.
p.value	logical; FALSE (default) Generates 100 independent random permutations to test results against and plots 95 percent confidence intervals along with all results.
print.map	logical; FALSE (default) Plots quadrant means, or p-value replicates.

Value

Returns the bi-variate "Correlation" and "Dependence" or correlation / dependence matrix for matrix input.

Note

For asymmetrical (asym = TRUE) matrices, directional dependence is returned as ([column variable] —> [row variable]).

Author(s)

Fred Viole, OVVO Financial Systems

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp

Examples

## Not run: 
set.seed(123)
x <- rnorm(100) ; y <- rnorm(100)
NNS.dep(x, y)

## Correlation / Dependence Matrix
x <- rnorm(100) ; y <- rnorm(100) ; z <- rnorm(100)
B <- cbind(x, y, z)
NNS.dep(B)

## End(Not run)

NNS documentation built on Nov. 28, 2023, 1:10 a.m.