Figure_3

Simulated time series of the three dimensions from the Lorenz system

datasets

Auto, Cross and Multi-dimensional recurrence quantification analysis.
Different methods for computing recurrence, cross vs. multidimensional
or profile iti.e., only looking at the diagonal recurrent points,
as well as functions for optimization and plotting are proposed.
in-depth measures of the whole cross-recurrence plot,
Please refer to Coco and others (2021) <doi:10.32614/RJ-2021-062>,
Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510>
and Wallot (2018) <doi: 10.1080/00273171.2018.1512846>
for further details about the method.

Moreno I Coco

crqa

Unidimensional and Multidimensional Methods for Recurrence
Quantification Analysis

Moreno  I. Coco

Dan  Mønster

Giuseppe Leonardi

Rick  Dale

Sebastian Wallot

James D. Dixon

John  C. Nash

Alexandra Paxton

Figure_3 function

A data frame with 2048 rows and 3 columns:<dl>
 <dt>V1</dt>
<dd>First dimension of the Lorenz system</dd> <dt>V2</dt>
<dd>Second dimension of the Lorenz system</dd> <dt>V3</dt>
<dd>Third dimension of the Lorenz system</dd> 
</dl>

Format

Simulated time series of the three dimensions from the Lorenz system — Figure_3

A data frame with 2048 rows and 3 columns:<dl>
 <dt>V1</dt>
<dd>First dimension of the Lorenz system</dd>

 <dt>V2</dt>
<dd>Second dimension of the Lorenz system</dd>

 <dt>V3</dt>
<dd>Third dimension of the Lorenz system</dd>

 
</dl>

Simulated time series of the three dimensions from the Lorenz system

Figure_3: Simulated time series of the three dimensions from the Lorenz system

Description

Usage

Arguments

Format