Identification of significant factors by an extension of ANOVA-PCA based on multi-block analysis
Identification of significant factors by an extension of ANOVA-PCA based on multi-block analysis
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DOI：
10.1016/j.chemolab.2010.05.005
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发表时间：
2011-04-15
影响因子：
3.9
通讯作者：
Rutledge, D. N.
中科院分区：
文献类型：
--
作者：
Bouveresse, D. Jouan-Rimbaud;Pinto, R. Climaco;Rutledge, D. N.
本文介绍了哈灵顿等人提出的用于确定实验设计中重要因素和相互作用的方差分析-主成分分析方法的改进。改进的方法使用多表分析的思想，并寻找不同的数据表，或数据块，由“方差分析-主成分分析方法的步骤”产生的共同的维度，以确定显着的因素。本文采用“公分量比重分析法”对计算得到的多块数据集进行分析。通过分析四个真实的数据集，将这种称为AComDim的新方法与标准的ANOVA-PCA方法进行了比较。在AComDim程序期间计算的参数使得能够计算F值以检查每个原始数据块的可变性是否显著大于噪声的可变性。(C)2010 Elsevier B. V.保留所有权利。
A modification of the ANOVA-PCA method, proposed by Harrington et al. to identify significant factors and interactions in an experimental design, is presented in this article. The modified method uses the idea of multiple table analysis, and looks for the common dimensions underlying the different data tables, or data blocks, generated by the "ANOVA-step" of the ANOVA-PCA method, in order to identify the significant factors. In this paper, the "Common Component and Specific Weights Analysis" method is used to analyse the calculated multi-block data set. This new method, called AComDim, was compared to the standard ANOVA-PCA method, by analysing four real data sets. Parameters computed during the AComDim procedure enable the computation of F-values to check whether the variability of each original data block is significantly greater than that of the noise. (C) 2010 Elsevier B.V. All rights reserved.