NNS (version 0.5.6)

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, print.map = FALSE)

Arguments

x

a numeric vector, matrix or data frame.

y

NULL (default) or a numeric vector with compatible dimsensions to x.

asym

logical; FALSE (default) Allows for asymmetrical dependencies.

print.map

logical; FALSE (default) Plots quadrant means.

Value

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

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)


## p-values for [NNS.dep]
x <- seq(-5, 5, .1); y <- x^2 + rnorm(length(x))


nns_cor_dep <- NNS.dep(x, y, print.map = TRUE)
nns_cor_dep

## Create permutations of y
y_p <- replicate(1000, sample.int(length(y)))

## Generate new correlation and dependence measures on each new permutation of y
nns.mc <- apply(y_p, 2, function(g) NNS.dep(x, y[g]))

## Store results
cors <- unlist(lapply(nns.mc, "[[", 1))
deps <- unlist(lapply(nns.mc, "[[", 2))

## View results
hist(cors)
hist(deps)

## Left tailed correlation p-value
cor_p_value <- LPM(0, nns_cor_dep$Correlation, cors)
cor_p_value

## Right tailed correlation p-value
cor_p_value <- UPM(0, nns_cor_dep$Correlation, cors)
cor_p_value

## Confidence Intervals
## For 95th percentile VaR (both-tails) see [LPM.VaR] and [UPM.VaR]
## Lower CI
LPM.VaR(.025, 0, cors)
## Upper CI
UPM.VaR(.025, 0, cors)

## Left tailed dependence p-value
dep_p_value <- LPM(0, nns_cor_dep$Dependence, deps)
dep_p_value

## Right tailed dependence p-value
dep_p_value <- UPM(0, nns_cor_dep$Dependence, deps)
dep_p_value
# }