entfuns

Computes a set of descriptive diagnostics for a binary outcome
 `y` against one or more predictors in `x`, including marginal probability,
 conditional probability, absolute and proportional differences between
 marginal and conditional probabilities, and analogous measures based on
. entropy.

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

entfuns function

<dl><dt>y</dt>
<dd>A binary outcome vector (0/1 or logical). Length `n`.</dd>
<dt>x</dt>
<dd>A data frame of binary predictors (columns). Must have `n` rows;
each column is analyzed separately against `y`.</dd></dl>

Arguments

entfuns — entfuns

<dl>

<dt>y</dt>
<dd>A binary outcome vector (0/1 or logical). Length `n`.</dd>


<dt>x</dt>
<dd>A data frame of binary predictors (columns). Must have `n` rows;
each column is analyzed separately against `y`.</dd>

</dl>

entfuns: entfuns

Description

Usage

Value

Arguments

Details

Examples