Getting Started with NNS: Correlation and Dependence"

knitr::opts_chunk$set(echo = TRUE)

Correlation and Dependence

The limitations of linear correlation are well known. Often one uses correlation, when dependence is the intended measure for defining the relationship between variables. NNS dependence NNS.dep is a signal:noise measure robust to nonlinear signals.

Below are some examples comparing NNS correlation NNS.cor and NNS.dep with the standard Pearson's correlation coefficient cor.

Linear Equivalence

x=seq(0,3,.01); y=2*x

NNS.dep(x,y, = T)

Nonlinear Relationship

x=seq(0,3,.01); y=x^10

NNS.dep(x,y, = T)


df<- data.frame(x=runif(10000,-1,1),y=runif(10000,-1,1))
df<- subset(df, (x^2 + y^2 <= 1 & x^2 + y^2 >= 0.95))
NNS.dep(df$x,df$y, = T)


If the user is so motivated, detailed arguments and proofs are provided within the following:

*Nonlinear Nonparametric Statistics: Using Partial Moments

*Deriving Nonlinear Correlation Coefficients from Partial Moments

*Beyond Correlation: Using the Elements of Variance for Conditional Means and Probabilities

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NNS documentation built on Nov. 17, 2017, 5:19 a.m.