elsa_fuzzy_vector

Calculate the Local Fuzzy ELSA statistic using a nblistw object

internal

Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed.
The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).

Jeremy Gelb

geocmeans

Implementing Methods for Spatial Fuzzy Unsupervised
Classification

Philippe Apparicio

elsa_fuzzy_vector function

<dl><dt>memberships</dt>
<dd>A membership matrix</dd>
<dt>nblistw</dt>
<dd>The spatial weight matrix (nblistw object from spdep)</dd>
<dt>matdist</dt>
<dd>A matrix representing the dissimilarity between the clusters. The matrix must
be squared and the diagonal must be filled with zeros.</dd></dl>

Arguments

Local Fuzzy ELSA statistic for vector — elsa_fuzzy_vector

<dl>

<dt>memberships</dt>
<dd>A membership matrix</dd>


<dt>nblistw</dt>
<dd>The spatial weight matrix (nblistw object from spdep)</dd>


<dt>matdist</dt>
<dd>A matrix representing the dissimilarity between the clusters. The matrix must
be squared and the diagonal must be filled with zeros.</dd>

</dl>

elsa_fuzzy_vector: Local Fuzzy ELSA statistic for vector

Description

Usage

Value

Arguments

Examples