NNS (version 11.3)

NNS.dep: 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)

Value

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

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.

Author

Fred Viole, OVVO Financial Systems

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" (ISBN: 1490523995)

Examples

if (FALSE) {
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)
}