Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R
Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R
复制标题
使用交叉递归量化分析 (CRQA)、对角交叉递归轮廓 (DCRP) 和多维递归量化分析 (MdRQA) 分析多元动力学 – R 教程
DOI：
10.3389/fpsyg.2018.02232
复制
发表时间：
2018
影响因子：
3.8
通讯作者：
G. Leonardi
中科院分区：
文献类型：
--
作者：
Sebastian Wallot;G. Leonardi
本文提供了交叉复发量化分析（CRQA）、对角交叉复发谱（DCRP）和多维复发量化分析（MdRQA）的实用、实践介绍。这些方法在认知和社会科学中越来越受欢迎，因为人们认识到许多行为和神经生理过程本质上依赖于时间，依赖于环境和社会背景。基于递归的方法特别适合于非平稳或具有复杂动态的时间序列，例如连续生理或运动数据的较长记录，但在符号数据的时间序列的情况下也很有用，例如在文本/口头转录或分类编码行为的情况下。在过去，它们被用来评估生理和行为测量的动态变化或耦合，例如在联合行动研究中确定双组或群体中个体之间行为的共同进化，或用于评估两个或多个时间序列之间耦合/相关性的强度。在本文中，我们为读者提供了一个概念介绍，然后逐步解释如何在R中执行分析，并总结了其应用程序的当前最佳实践。
This paper provides a practical, hands-on introduction to cross-recurrence quantification analysis (CRQA), diagonal cross-recurrence profiles (DCRP), and multidimensional recurrence quantification analysis (MdRQA) in R. These methods have enjoyed increasing popularity in the cognitive and social sciences since a recognition that many behavioral and neurophysiological processes are intrinsically time dependent and reliant on environmental and social context has emerged. Recurrence-based methods are particularly suited for time-series that are non-stationary or have complicated dynamics, such as longer recordings of continuous physiological or movement data, but are also useful in the case of time-series of symbolic data, as in the case of text/verbal transcriptions or categorically coded behaviors. In the past, they have been used to assess changes in the dynamics of, or coupling between physiological and behavioral measures, for example in joint action research to determine the co-evolution of the behavior between individuals in dyads or groups, or for assessing the strength of coupling/correlation between two or more time-series. In this paper, we provide readers with a conceptual introduction, followed by a step-by-step explanation on how the analyses are performed in R with a summary of the current best practices of their application.
影响因子：
2.2
作者：
Nomikou, Iris;Leonardi, Giuseppe;Raczaszek-Leonardi, Joanna
通讯作者：
Raczaszek-Leonardi, Joanna
DOI：
10.1037/0096-1523.29.2.326
发表时间：
2003-04-01
影响因子：
2.1
作者：
Shockley, K;Santana, MV;Fowler, CA
通讯作者：
Fowler, CA