Computes the mutual information (MI) between an outcome `y` and
a predictor `x`, using the standard definition: $$MI(X, Y) = H(X) + H(Y)
- H(X, Y),$$

Create sets of variables based on a mutual
information approach. In this context, a set is a collection of
distinct elements (e.g., variables) that can also be treated as a
single entity.  Mutual information, a concept from probability theory,
quantifies the dependence between two variables by expressing how much
information about one variable can be gained from observing the other.
Furthermore, you can analyze, and visualize
these sets in order to better understand the relationships among
variables.

Nicolas Leenaerts

setweaver

Building Sets of Variables in a Probabilistic Framework

Aaron Fisher

mi function

<dl><dt>y</dt>
<dd>Outcome vector (binary: 0/1 or logical).</dd>
<dt>x</dt>
<dd>Predictor vector (binary: 0/1 or logical). Must be the same length
as `y`.</dd></dl>

Arguments

mi — mi

<dl>

<dt>y</dt>
<dd>Outcome vector (binary: 0/1 or logical).</dd>


<dt>x</dt>
<dd>Predictor vector (binary: 0/1 or logical). Must be the same length
as `y`.</dd>

</dl>

mi: mi

Description

Usage

Value

Arguments

Examples