exposure

Returns the pairwise exposure indices between groups

Computes segregation indices, including the Index of Dissimilarity,
as well as the information-theoretic indices developed by
Theil (1971) <isbn:978-0471858454>, namely
the Mutual Information Index (M) and Theil's Information Index (H).
The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x>
and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>,
is a measure of segregation that is highly decomposable. The package provides
tools to decompose the index by units and groups (local segregation),
and by within and between terms. The package also provides a method to decompose
differences in segregation as described by Elbers (2021) <doi:10.1177/0049124121986204>.
The package includes standard error estimation by bootstrapping, which also corrects for
small sample bias. The package also contains functions for visualizing segregation patterns.

Benjamin Elbers

segregation

Entropy-Based Segregation Indices

exposure function

<dl><dt>data</dt>
<dd>A data frame.</dd>
<dt>group</dt>
<dd>A categorical variable
contained in <code>data</code>. Defines the first dimension
over which segregation is computed.</dd>
<dt>unit</dt>
<dd>A vector of variables
contained in <code>data</code>. Defines the second dimension
over which segregation is computed.</dd>
<dt>weight</dt>
<dd>Numeric. (Default <code>NULL</code>)</dd></dl>

Arguments

Calculates pairwise exposure indices — exposure

<dl>

<dt>data</dt>
<dd>A data frame.</dd>


<dt>group</dt>
<dd>A categorical variable
contained in <code>data</code>. Defines the first dimension
over which segregation is computed.</dd>


<dt>unit</dt>
<dd>A vector of variables
contained in <code>data</code>. Defines the second dimension
over which segregation is computed.</dd>


<dt>weight</dt>
<dd>Numeric. (Default <code>NULL</code>)</dd>

</dl>

exposure: Calculates pairwise exposure indices

Description

Usage

Value

Arguments