MIC: Maximal information coefficient (MIC)

In AnaBPazos/AlterCorr: ALTERNATIVES TO THE PEARSON CORRELATION

Description

This function calculates the Maximal information coefficient (MIC). Calculate the dependence of pairs of variables. The MIC belongs to a larger classification of nonparametric exploration statistics based on maximum information (MINE).

Usage

MIC(x, y, R = 100, ...)

Arguments

x	A vector, matrix or numeric data frame
y	A vector, matrix or numeric data frame
R	Number of replications that will be used to calculate the p-value
...	Additional parameters defined in the codemine function of the codeminerva package.

Details

It also provides the p-value for the independence hypothesis, that are generated by permuting the data and seeing how likely it is that the observed MIC value arises from the perturbed data.

Examples

#Lineal dependence
x<-rnorm(200, sd=2.5)
y<-2*x+rnorm(200, sd=2.5)
MIC(x, y, R=100)

#No dependency
x<-rnorm(200, mean=2, sd =1.8)
y<-rnorm(200, mean=0, sd =2.5)
MIC(x, y, R=100)

AnaBPazos/AlterCorr documentation built on May 20, 2019, 4:24 p.m.