IndepTest (version 0.2.0)

MINTav: 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

2017arXiv171106642BIndepTest

Examples

# NOT RUN {
# 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)
# }
# NOT RUN {
# }