首页> 外文OA文献 >Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R
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Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R

机译:使用交叉复发量化分析(CRQA),对角线交换分析(DCRP)和多维复发量化分析(MDRQA)分析多变量动力学 - r中的教程

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摘要

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.
机译:本文在认知提供了一种实用,动手地介绍了R.交叉递归定量分析(CRQA),斜向交叉复发型材(直流反接),和多维递归定量分析(MdRQA)这些方法都享有日益普及和因为承认社会科学的许多行为和神经生理过程本质上是依赖于时间和依赖于环境和社会环境已经出现。基于复发的方法特别适用于时间序列是非平稳或具有复杂动力学,如连续的生理或移动数据的较长的记录,但是,可以在的时间序列的符号数据的情况下是有用的,如在案例文/口头记录或断然编码的行为。在过去,他们已经被用来评估生理和行为的措施之间的动态变化,或联轴器,例如在联合行动的研究,以确定二人组合或群体的个人之间的行为的协同进化,或评估两个或更多个时间序列之间的耦合/相关性的强度。在本文中,我们提供一个概念性的介绍读者,其次是就如何分析是R中与他们的应用程序的当前最佳实践的总结进行一步一步的解释。

著录项

  • 作者单位
  • 年度 2018
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 入库时间 2022-08-20 21:58:35

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