Jonas M B Haslbeck, Sacha Epskamp, Lourens J Waldorp
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引用次数: 0
摘要
多层次矢量自回归(VAR)模型已成为分析多个受试者时间序列数据的常用工具。许多研究旨在调查多层次 VAR 模型在不同组别(如患者和健康对照组)之间的差异。然而,目前还没有一种简单适用的方法来推断这种组间差异。在此,我们将介绍两种用于进行此类推断的标准检验方法:参数检验和非参数置换检验。我们解释了这两种检验的原理,提供了基于流行的 R 软件包 mlVAR 的实现方法,并通过模拟研究评估了它们在类似实证研究的情况下恢复群体差异的性能。最后,我们提供了一个完全可重现的 R 教程,利用新的 R 软件包 mnet 测试情感测量数据集中的群体差异。
Testing for group differences in multilevel vector autoregressive models.
Multilevel Vector Autoregressive (VAR) models have become a popular tool for analyzing time series data from multiple subjects. Many studies aim to investigate differences in multilevel VAR models between groups, such as patients and healthy controls. However, there is currently no easily applicable method to make inferences about such group differences. Here, we present two standard tests for making such inferences: a parametric test and a nonparametric permutation test. We explain the rationale for both tests, provide an implementation based on the popular R-package mlVAR, and evaluate their performance in recovering group differences in scenarios resembling empirical research using a simulation study. Finally, we provide a fully reproducible R-tutorial on testing for group differences in a dataset of emotion measures using the new R-package mnet.
期刊介绍:
Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.