NNS.dep {NNS}R Documentation

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,
  ncores = NULL
)

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.

ncores

integer; value specifying the number of cores to be used in the parallelized procedure. If NULL (default), the number of cores to be used is equal to the number of cores of the machine - 1.

Value

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

Note

NNS.cor has been deprecated (NNS >= 0.5.4) and can be called via NNS.dep.

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)

[Package NNS version 0.8.70 Index]