Identification of significant factors by an extension of ANOVA'PCA based on multi-block analysis

D. Jouan-Rimbaud Bouveresse, R. Climaco Pinto, Leigh Schmidtke, N. Locquet, D. N. Rutledge

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)


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 multiblock 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.
Original languageEnglish
Pages (from-to)173-182
Number of pages10
JournalChemometrics and Intelligent Laboratory Systems
Issue number2
Early online date2010
Publication statusPublished - Apr 2011


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