MINTav {IndepTest}

MINTav

Description

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

Usage

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

Berrett, T. B. and Samworth R. J. (2017). “Nonparametric independence testing via mutual information.” ArXiv e-prints. 1711.06642.

Examples

# 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)