compress

Given a data set that identifies suitable neighbors for merging,
this function will merge units iteratively, where in each iteration
the neighbors with the smallest reduction in terms of total M will be merged.

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

compress 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 categorical variable
contained in <code>data</code>. Defines the second dimension
over which segregation is computed.</dd>
<dt>weight</dt>
<dd>Numeric. Only frequency weights are allowed.
(Default <code>NULL</code>)</dd>
<dt>neighbors</dt>
<dd>Either a data frame or a character. If data frame, then
it needs exactly two columns, where each row identifies
a set of "neighbors" that may be merged.
If "local", considers the <code>n_neighbors</code> closest neighbors
in terms of local segregation.
If "all", all units are considered as possible neighbors. This
may be very time-consuming.</dd>
<dt>n_neighbors</dt>
<dd>Only relevant if <code>neighbors</code> is <code>"local"</code>.</dd>
<dt>max_iter</dt>
<dd>Maximum number of iterations (Default <code>Inf</code>)</dd></dl>

Arguments

Compresses a data matrix based on mutual information (segregation) — compress

<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 categorical variable
contained in <code>data</code>. Defines the second dimension
over which segregation is computed.</dd>


<dt>weight</dt>
<dd>Numeric. Only frequency weights are allowed.
(Default <code>NULL</code>)</dd>


<dt>neighbors</dt>
<dd>Either a data frame or a character. If data frame, then
it needs exactly two columns, where each row identifies
a set of "neighbors" that may be merged.
If "local", considers the <code>n_neighbors</code> closest neighbors
in terms of local segregation.
If "all", all units are considered as possible neighbors. This
may be very time-consuming.</dd>


<dt>n_neighbors</dt>
<dd>Only relevant if <code>neighbors</code> is <code>"local"</code>.</dd>


<dt>max_iter</dt>
<dd>Maximum number of iterations (Default <code>Inf</code>)</dd>

</dl>

compress: Compresses a data matrix based on mutual information (segregation)

Description

Usage

Value

Arguments