Cross-recurrence quantification analysis of categorical and continuous time series: an R package.
Cross-recurrence quantification analysis of categorical and continuous time series: an R package.
复制标题
DOI：
10.3389/fpsyg.2014.00510
复制
发表时间：
2014
影响因子：
3.8
通讯作者：
Dale R
中科院分区：
文献类型：
--
作者：
Coco MI;Dale R
本文介绍了R包crqa进行交叉递归量化分析的两个时间序列的分类或连续的性质。行为信息流，从眼球运动到语言元素，随着时间的推移而展开。当两个人互动时，比如在谈话中，他们经常会相互适应，导致这些行为水平表现出反复出现的状态。例如，在对话中，对话者通过交换互动线索（微笑、微笑、手势、措辞等）来适应彼此，为了让我们密切捕捉动态互动的进展，并揭示两个人之间的耦合程度，我们需要量化在这些层面上发生的重复程度。crqa中可用的方法将允许认知科学的研究人员提出这样的问题：在某种分析水平上，两个人的重复性有多大，一个人最大限度地匹配另一个人的特征滞后时间是多少，或者一个人是否在领导另一个人。首先，我们设置的理论基础，以了解“相关性”和“共访”之间的差异时，比较两个时间序列，使用聚合或交叉递归的方法。然后，我们将更正式地描述交叉递归的原理，并使用当前的包展示如何应用它们进行分析。最后，我们比较了crqa R软件包的计算效率，结果的一致性，与基准MATLAB工具箱crptoolbox（Marwan，）。我们在这两个层面上显示了两个库之间的完美可比性。
This paper describes the R package crqa to perform cross-recurrence quantification analysis of two time series of either a categorical or continuous nature. Streams of behavioral information, from eye movements to linguistic elements, unfold over time. When two people interact, such as in conversation, they often adapt to each other, leading these behavioral levels to exhibit recurrent states. In dialog, for example, interlocutors adapt to each other by exchanging interactive cues: smiles, nods, gestures, choice of words, and so on. In order for us to capture closely the goings-on of dynamic interaction, and uncover the extent of coupling between two individuals, we need to quantify how much recurrence is taking place at these levels. Methods available in crqa would allow researchers in cognitive science to pose such questions as how much are two people recurrent at some level of analysis, what is the characteristic lag time for one person to maximally match another, or whether one person is leading another. First, we set the theoretical ground to understand the difference between “correlation” and “co-visitation” when comparing two time series, using an aggregative or cross-recurrence approach. Then, we describe more formally the principles of cross-recurrence, and show with the current package how to carry out analyses applying them. We end the paper by comparing computational efficiency, and results’ consistency, of crqa R package, with the benchmark MATLAB toolbox crptoolbox (Marwan,). We show perfect comparability between the two libraries on both levels.
登录
查看更多内容
影响因子：
2.2
作者：
Dale, Rick;Warlaumont, Anne S.;Richardson, Daniel C.
通讯作者：
Richardson, Daniel C.
影响因子：
2.5
作者：
Richardson, DC;Dale, R
通讯作者：
Dale, R
影响因子：
2.5
作者：
Louwerse, Max M.;Dale, Rick;Jeuniaux, Patrick
通讯作者：
Jeuniaux, Patrick
影响因子：
3.4
作者：
SCHOBER, MF
通讯作者：
SCHOBER, MF
影响因子：
1.7
作者：
Paxton, Alexandra;Dale, Rick
通讯作者：
Dale, Rick