Figure_1

Three time series (1250 observations) organised as columns and simulating data from: a periodical sine wave, one of the dimensions of the Lorenz attractor and a white noise signal.

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_1 function

A data frame with 1250 rows and 3 columns:<dl>
 <dt>sinus</dt>
<dd>A periodical sine wave</dd> <dt>lorenz</dt>
<dd>One dimension of a Lorenz attractor</dd> <dt>wnoise</dt>
<dd>White noise</dd>
 
</dl>

Format

Eye-movement categorical time-series — Figure_1

A data frame with 1250 rows and 3 columns:<dl>
 <dt>sinus</dt>
<dd>A periodical sine wave</dd>

 <dt>lorenz</dt>
<dd>One dimension of a Lorenz attractor</dd>

 <dt>wnoise</dt>
<dd>White noise</dd>


 
</dl>

Eye-movement categorical time-series

Figure_1: Eye-movement categorical time-series

Description

Usage

Arguments

Format