calcLaggedData

Calculate Wx, the spatially lagged version of x, by a neighbouring matrix W.

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

calcLaggedData function

<dl><dt>x</dt>
<dd>A dataframe with only numeric columns</dd>
<dt>nblistw</dt>
<dd>The listw object (spdep like) used to calculate WY</dd>
<dt>method</dt>
<dd>A string indicating if a classical lag must be used
("mean") or if a weighted median must be used ("median")</dd></dl>

Arguments

Lagged Data — calcLaggedData

<dl>

<dt>x</dt>
<dd>A dataframe with only numeric columns</dd>


<dt>nblistw</dt>
<dd>The listw object (spdep like) used to calculate WY</dd>


<dt>method</dt>
<dd>A string indicating if a classical lag must be used
("mean") or if a weighted median must be used ("median")</dd>

</dl>

calcLaggedData: Lagged Data

Description

Usage

Value

Arguments

Examples