joint

Computes the joint probability \(P(X = 1, Y = 1)\) for two binary vectors
`x` and `y`. Rows with missing values in either vector are excluded.

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

joint function

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

Arguments

joint — joint

<dl>

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


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

</dl>

joint: joint

Description

Usage

Value

Arguments

Examples