diff_multidim

Calculates average Euclidean distance between means in arbitrary dimensions.

Implements cluster-polarization coefficient for measuring distributional
polarization in single or multiple dimensions, as well as associated functions.
Contains support for hierarchical clustering, k-means, partitioning around medoids,
density-based spatial clustering with noise, and manually imposed cluster membership.
Mehlhaff (2024) <doi:10.1017/S0003055423001041>.

Isaac Mehlhaff

Implementation of Cluster-Polarization Coefficient

diff_multidim function

<dl><dt>data</dt>
<dd>a numeric vector or <code>n x k</code> matrix or data frame containing a vector
identifying cluster membership for each observation, to be passed to
<code>clusters</code> argument.</dd>
<dt>cols</dt>
<dd>columns of <code>data</code> to be used in difference-in-means calculation.</dd>
<dt>clusters</dt>
<dd>column of <code>data</code> indicating cluster membership for each
observation.</dd></dl>

Arguments

Multidimensional Difference-in-Means — diff_multidim

<dl>

<dt>data</dt>
<dd>a numeric vector or <code>n x k</code> matrix or data frame containing a vector
identifying cluster membership for each observation, to be passed to
<code>clusters</code> argument.</dd>


<dt>cols</dt>
<dd>columns of <code>data</code> to be used in difference-in-means calculation.</dd>


<dt>clusters</dt>
<dd>column of <code>data</code> indicating cluster membership for each
observation.</dd>

</dl>

diff_multidim: Multidimensional Difference-in-Means

Description

Usage

Value

Arguments

Examples