This function empirically estimates the Mutual Information from a table of counts using the observed frequencies.
miData(freqs.table, method = c("mi.raw", "mi.raw.pc"))
a table of counts.
a character determining if the Mutual Information should be normalized.
The mutual information estimation is computed from the observed frequencies through a plugin estimator based on entropy.
The plugin estimator is I(X, Y) = H (X) + H(Y) - H(X, Y), where H() is the entropy computed with
Mutual information estimate.
Cover, Thomas M, and Joy A Thomas. (2012). "Elements of Information Theory". John Wiley & Sons.
## Generate random variable Y <- rnorm(n = 100, mean = 0, sd = 2) X <- rnorm(n = 100, mean = 5, sd = 2) dist <- list(Y="gaussian", X="gaussian") miData(discretization(data.df = cbind(X,Y), data.dists = dist, discretization.method = "fd", nb.states = FALSE), method = "mi.raw")
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