abn (version 1.3)

miData: Computes an Empirical Estimation of the Entropy from a Table of Counts

Description

This function empirically estimates the Mutual Information from a table of counts using the observed frequencies.

Usage

miData(freqs.table, method = c("mi.raw", "mi.raw.pc"))

Arguments

freqs.table

a table of counts.

method

a character determining if the Mutual Information should be normalized.

Value

Mutual information estimate.

Details

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 entropyData.

Examples

# NOT RUN {
## 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(freqs.table = discretization(data.df = cbind(X,Y),
data.dists = dist,
discretization.method = "fd",
nb.states = FALSE),method = "mi.raw")
# }