Supervised Multiblock Analysis in R with the ade4 Package
Supervised Multiblock Analysis in R with the ade4 Package
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DOI：
10.18637/jss.v086.i01
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发表时间：
2018-08-01
影响因子：
5.8
通讯作者：
Dray, Stephane
中科院分区：
文献类型：
--
作者：
Bougeard, Stephanie;Dray, Stephane
本文提出了两种使用 R 语言对多块数据进行统计分析的新颖方法。它专为组织在 (K + 1) 个块（即表）中的数据而设计，该块由响应变量块组成，并由大量解释变量进行解释，这些解释变量被分为 K 个有意义的块。所有变量（解释变量和相关变量）都是针对同一个人进行测量的。包括两种在实践中有用的多块方法，即多块偏最小二乘回归和带有工具变量的多块主成分分析。所提出的新方法包含在 ade4 包中，由于其多种多变量方法而被广泛使用。这些方法一方面可供统计学家使用，另一方面也可供各个领域的用户使用，因为从多块处理导出的所有值都是可用的。一些相关的解释工具也被开发出来。最后使用整体图形显示总结主要结果。本文按照标准多块过程的不同步骤进行组织，每个步骤对应于特定的 R 函数。所有这些步骤都通过对真实流行病学数据集的分析来说明。
This paper presents two novel statistical analyses of multiblock data using the R language. It is designed for data organized in (K + 1) blocks (i.e., tables) consisting of a block of response variables to be explained by a large number of explanatory variables which are divided into K meaningful blocks. All the variables - explanatory and dependent are measured on the same individuals. Two multiblock methods both useful in practice are included, namely multiblock partial least squares regression and multiblock principal component analysis with instrumental variables. The proposed new methods are included within the ade4 package widely used thanks to its great variety of multivariate methods. These methods are available on the one hand for statisticians and on the other hand for users from various fields in the sense that all the values derived from the multiblock processing are available. Some relevant interpretation tools are also developed. Finally the main results are summarized using overall graphical displays. This paper is organized following the different steps of a standard multiblock process, each corresponding to specific R functions. All these steps are illustrated by the analysis of real epidemiological datasets.