miData | R Documentation |

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"))

`freqs.table` |
a table of counts. |

`method` |
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 `entropyData`

.

Mutual information estimate.

Gilles Kratzer

Cover, Thomas M, and Joy A Thomas. (2012). "Elements of Information Theory". John Wiley & Sons.

`discretization`

## 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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