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NNS Dependence

Source: R/Dependence.R
NNS.dep.Rd

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]).

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" (ISBN: 1490523995, 2nd edition: https://ovvo-financial.github.io/NNS/book/)

Author

Fred Viole, OVVO Financial Systems

Examples

if (FALSE) { # \dontrun{
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)
} # }