in_ties_loops

Are individuals that are in locations with a large inflow more likely to stay
in their current location?

internal

Implements the method to analyse weighted mobility networks or distribution networks as outlined in:
Block, P., Stadtfeld, C., & Robins, G. (2022) <doi:10.1016/j.socnet.2021.08.003>.
The purpose of the model is to analyse the structure of mobility,
incorporating exogenous predictors pertaining to individuals and locations
known from classical mobility analyses, as well as modelling emergent mobility
patterns akin to structural patterns known from the statistical analysis of social networks.

Per Block

MoNAn

Mobility Network Analysis

Christoph Stadtfeld

Nico Keiser

Marion Hoffman

in_ties_loops function

<dl><dt>dep.var</dt>
<dd></dd>
<dt>state</dt>
<dd></dd>
<dt>cache</dt>
<dd></dd>
<dt>i</dt>
<dd></dd>
<dt>j</dt>
<dd></dd>
<dt>edge</dt>
<dd></dd>
<dt>update</dt>
<dd></dd>
<dt>getTargetContribution</dt>
<dd></dd></dl>

Arguments

in_ties_loops — in_ties_loops

<dl>

<dt>dep.var</dt>
<dd></dd>


<dt>state</dt>
<dd></dd>


<dt>cache</dt>
<dd></dd>


<dt>i</dt>
<dd></dd>


<dt>j</dt>
<dd></dd>


<dt>edge</dt>
<dd></dd>


<dt>update</dt>
<dd></dd>


<dt>getTargetContribution</dt>
<dd></dd>

</dl>

in_ties_loops: in_ties_loops

Description

Usage

Value

Arguments