MINTperm {IndepTest}

MINTknown

Description

Performs an independence test without knowledge of either marginal distribution using permutations.

Usage

MINTperm(x, y, k, w = FALSE, B = 1000)

Arguments

x

The n \times d_X data matrix of X values.

y

The n \times d_Y data matrix of Y values.

k

The value of k to be used for estimation of the joint entropy H(X,Y).

w

The weight vector to used for estimation of the joint entropy H(X,Y), with the same options as for the KLentropy function.

B

The number of permutations to use, 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)
MINTperm(x,y,k=20,B=100)
# Dependent univariate normal data
library(mvtnorm)
data=rmvnorm(1000,sigma=matrix(c(1,0.5,0.5,1),ncol=2))  
MINTperm(data[,1],data[,2],k=20,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)
MINTperm(data[,1:3],data[,4],k=20,w=TRUE,B=100)