Department of Methodology and Statistics, TSB, Tilburg University, PO Box 90153, 5000LE, Tilburg, The Netherlands.
Behav Res Methods. 2019 Oct;51(5):2268-2289. doi: 10.3758/s13428-018-1163-z.
This article introduces a package developed for R (R Core Team, 2017) for performing an integrated analysis of multiple data blocks (i.e., linked data) coming from different sources. The methods in this package combine simultaneous component analysis (SCA) with structured selection of variables. The key feature of this package is that it allows to (1) identify joint variation that is shared across all the data sources and specific variation that is associated with one or a few of the data sources and (2) flexibly estimate component matrices with predefined structures. Linked data occur in many disciplines (e.g., biomedical research, bioinformatics, chemometrics, finance, genomics, psychology, and sociology) and especially in multidisciplinary research. Hence, we expect our package to be useful in various fields.
本文介绍了一个为 R 开发的软件包（R Core Team，2017），用于对来自不同来源的多个数据块（即链接数据）进行综合分析。该软件包中的方法将同时成分分析（SCA）与变量的结构化选择相结合。该软件包的主要特点是，它允许（1）识别跨所有数据源共享的共同变化和与一个或几个数据源相关的特定变化，以及（2）灵活地用预定义结构估计成分矩阵。链接数据出现在许多学科中（例如，生物医学研究、生物信息学、化学计量学、金融、基因组学、心理学和社会学），特别是在多学科研究中。因此，我们预计我们的软件包将在各个领域中都有用。
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