miData {abn} | R Documentation |

## 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. |

### 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`

.

### Value

Mutual information estimate.

integer

### References

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

### See Also

### Examples

```
## 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")
```

[Package

*abn*version 3.1.1 Index]