undecidedUnits

Identify the observation for which the classification is uncertain

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

undecidedUnits function

<dl><dt>belongmatrix</dt>
<dd>The membership matrix obtained at the end of the algorithm</dd>
<dt>tol</dt>
<dd>A float indicating the minimum required level of membership to be
not considered as undecided</dd>
<dt>out</dt>
<dd>The format of the output vector. Default is "character". If
"numeric", then the undecided units are set to -1.</dd></dl>

Arguments

Undecided observations — undecidedUnits

<dl>

<dt>belongmatrix</dt>
<dd>The membership matrix obtained at the end of the algorithm</dd>


<dt>tol</dt>
<dd>A float indicating the minimum required level of membership to be
not considered as undecided</dd>


<dt>out</dt>
<dd>The format of the output vector. Default is "character". If
"numeric", then the undecided units are set to -1.</dd>

</dl>

undecidedUnits: Undecided observations

Description

Usage

Value

Arguments

Examples