The MINERVA package provide a function to perform the Maximal Information Coefficient (MIC). The description of the package stipulates that the function mine (x,y) works only with 2 matrices A and B of the same size.
Here, I would like to obtain the MIC coefficient value obtained from the correlation of two A and B matrices of different size, respectfully, A is n by m and B is n by z, with n being the number of observations (rows). In other words, my aim is to obtain a C matrix of m x z , which returns, for each value, give the MIC correlation coefficient values (and, if possible, the associated P value, if any).
I provide an example of what I want with the Pearson correlation.
set.seed(1)
x <- matrix(rnorm(20), nrow=5, ncol=10)
y <- matrix(rnorm(15), nrow=5, ncol=20)
P <- cor(x, y=y)
I mailed one author of the MINERVA package without success, is there any way I can apply the mine function to obtain the desired m by z correlation?
Let me answer to my own post. In the code below, I use the loop function, which may be not the smartest/fastest way to to do it, but it work as expected.
library(minerva)
set.seed(1)
x <- matrix(rnorm(20), nrow=5, ncol=10)
y <- matrix(rnorm(15), nrow=5, ncol=20)
Result = matrix(ncol = ncol(y),nrow = ncol(x))
for(i in 1:ncol(x))
{Thisvar = x[,i]
print(i)
for(k in 1:ncol(y))
{Thisvar2 = y[,k]
res = mine(Thisvar,Thisvar2, master=TRUE, use="all.obs")
Result[i,k] = res$MIC
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