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

## Description

Performs an independence test when it is assumed that the marginal distribution of Y is known and can be simulated from.

## Usage

 1 MINTknown(x, y, k, ky, w = FALSE, wy = FALSE, y0) 

## 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). ky The value of k to be used for estimation of the marginal entropy H(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. wy The weight vector to used for estimation of the marginal entropy H(Y), with the same options as for the KLentropy function. y0 The data matrix of simulated Y values.

## 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 12 library(mvtnorm) x=rnorm(1000); y=rnorm(1000); # Independent univariate normal data MINTknown(x,y,k=20,ky=30,y0=rnorm(100000)) library(mvtnorm) # Dependent univariate normal data data=rmvnorm(1000,sigma=matrix(c(1,0.5,0.5,1),ncol=2)) # Dependent multivariate normal data MINTknown(data[,1],data[,2],k=20,ky=30,y0=rnorm(100000)) 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) MINTknown(data[,1:3],data[,4],k=20,ky=30,w=TRUE,wy=FALSE,y0=rnorm(100000)) 

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