entropy

Returns marginal entropy for binary variables

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

entropy function

<dl><dt>x</dt>
<dd>A binary vector (numeric coded as 0/1 or logical). Must be length &gt;= 1.</dd></dl>

Arguments

entropy — entropy

<dl>

<dt>x</dt>
<dd>A binary vector (numeric coded as 0/1 or logical). Must be length &gt;= 1.</dd>

</dl>

entropy: entropy

Description

Usage

Value

Arguments

Examples