Figure_6

Speed (time in seconds) and memory (peak RAM in MB) performance of crqa() 
 and piecewiseRQA() for simulated data of increasing size 
 (from 3000 to 7000 data points), compared in blocks of different 
 sizes (from 1000 to 6500 in increments of 500)

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  Monster

Giuseppe Leonardi

Rick  Dale

Sebastian Wallot

James D. Dixon

John  C. Nash

Alexandra Paxton

Figure_6 function

A data frame with 28 rows and 2 variables:<dl>
 <dt>speed</dt>
<dd>The time in seconds to perform a crqa() analysis</dd> <dt>memory</dt>
<dd>The peak MB occupied in the RAM to perform a crqa() analysis</dd> <dt>typeRQA</dt>
<dd>Whether the analysis contained all data points (full) or was
 computed piecewise (piece)</dd> <dt>datapoint</dt>
<dd>The number of datapoints</dd> <dt>blocksize</dt>
<dd>The dimension of the block</dd>
 
</dl>

Format

Figure_6 — Figure_6

A data frame with 28 rows and 2 variables:<dl>
 <dt>speed</dt>
<dd>The time in seconds to perform a crqa() analysis</dd>

 <dt>memory</dt>
<dd>The peak MB occupied in the RAM to perform a crqa() analysis</dd>

 <dt>typeRQA</dt>
<dd>Whether the analysis contained all data points (full) or was
 computed piecewise (piece)</dd>

 <dt>datapoint</dt>
<dd>The number of datapoints</dd>

 <dt>blocksize</dt>
<dd>The dimension of the block</dd>


 
</dl>

Figure_6: Figure_6

Description

Usage

Arguments

Format