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 it -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 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

Recurrence Quantification Analysis for Categorical and
Continuous Time-Series

Moreno  I. Coco

Dan  Monster

Giuseppe Leonardi

Rick  Dale

Sebastian Wallot

James D. Dixon

John  C. Nash

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