# MINTav: MINTav In IndepTest: Nonparametric Independence Tests Based on Entropy Estimation

## Description

Performs an independence test without knowledge of either marginal distribution using permutations and averaging over a range of values of k.

## Usage

 1 MINTav(x, y, K, B = 1000) 

## Arguments

 x The n \times d_{X} data matrix of the X values. y The n \times d_{Y} data matrix of the Y values. K The vector of values of k to be considered for estimation of the joint entropy H(X,Y). B The number of permutations to use for the test, set at 1000 by default.

## Value

The p-value corresponding the independence test carried out.

## References

\insertRef

2017arXiv171106642BIndepTest

## Examples

  1 2 3 4 5 6 7 8 9 10 11 # Independent univariate normal data x=rnorm(1000); y=rnorm(1000); MINTav(x,y,K=1:200,B=100) # Dependent univariate normal data library(mvtnorm); data=rmvnorm(1000,sigma=matrix(c(1,0.5,0.5,1),ncol=2)) MINTav(data[,1],data[,2],K=1:200,B=100) # Dependent multivariate normal data Sigma=matrix(c(1,0,0,0,0,1,0,0,0,0,1,0.5,0,0,0.5,1),ncol=4); data=rmvnorm(1000,sigma=Sigma) MINTav(data[,1:3],data[,4],K=1:50,B=100) 

IndepTest documentation built on May 1, 2019, 10:24 p.m.