par_nn_metrics

This function calculates the bearing angle and distance from
a focal individual to its nearest neighbor at the given timestep.

internal

Provides a pipeline for the comparative analysis of
collective movement data (e.g. fish schools, bird flocks, baboon troops) by
processing 2-dimensional positional data (x,y,t) from GPS trackers or computer
vision tracking systems, discretizing events of collective motion, calculating
a set of established metrics that characterize each event, and placing the
events in a multi-dimensional swarm space constructed from these metrics.
The swarm space concept, the metrics and data sets included are described in:
Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon,
Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King
Andrew J. (2023) <doi:10.1098/rstb.2022.0068>.

Marina Papadopoulou

swaRmverse

Swarm Space Creation

Simon Garnier

par_nn_metrics function

<dl><dt>per_time</dt>
<dd>A groups positional data at a single timestep.
Columns must include: <code>id</code>, <code>t</code>, <code>head</code>, <code>x</code>, <code>y</code>.</dd>
<dt>geo</dt>
<dd>Logical, whether positions are geographic coordinates, default = FALSE.</dd></dl>

Arguments

Marina Papadopoulou <a href="/link/m.papadopoulou.rug%40gmail.com?package=swaRmverse&version=0.1.0" data-mini-rdoc="swaRmverse::m.papadopoulou.rug@gmail.com">m.papadopoulou.rug@gmail.com</a>

Author

Adding Motion Properties in Parallel - Verbose — par_nn_metrics

<dl>

<dt>per_time</dt>
<dd>A groups positional data at a single timestep.
Columns must include: <code>id</code>, <code>t</code>, <code>head</code>, <code>x</code>, <code>y</code>.</dd>


<dt>geo</dt>
<dd>Logical, whether positions are geographic coordinates, default = FALSE.</dd>

</dl>

Marina Papadopoulou <a href='mailto:m.papadopoulou.rug@gmail.com'>m.papadopoulou.rug@gmail.com</a>

par_nn_metrics: Adding Motion Properties in Parallel - Verbose

Description

Usage

Value

Arguments

Author