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Analysis of variance – simultaneous component analysis (ASCA or ANOVA–SCA) is a method that partitions variation and enables interpretation of these partitions by SCA, a method that is similar to principal components analysis (PCA). This method is a multivariate or even megavariate extension of analysis of variance (ANOVA). The variation partitioning is similar to ANOVA. Each partition matches all variation induced by an effect or factor, usually a treatment regime or experimental condition. The calculated effect partitions are called effect estimates. Because even the effect estimates are multivariate, interpretation of these effects estimates is not intuitive. By applying SCA on the effect estimates one gets a simple interpretable result.〔Smilde, Age K.; Jansen, Jeroen J.; Hoefsloot, Huub C. J.; Lamers, Robert-Jan A. N.; van der Greef, Jan; Timmerman, Marieke E. (2005) "ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data", ''Bioinformatics'', 21 (13), 3043-3048. 〕〔Jansen, J. J.; Hoefsloot, H. C. J.; van der Greef, J.; Timmerman, M. E.; Westerhuis, J. A.;Smilde, A. K. (2005) "ASCA: analysis of multivariate data obtained from an experimental design". ''Journal of Chemometrics'', 19: 469–481. 〕〔Daniel J Vis , Johan A Westerhuis , Age K Smilde: Jan van der Greef (2007) "Statistical validation of megavariate effects in ASCA", ''BMC Bioinformatics" , 8:322 〕 In case of more than one effect this method estimates the effects in such a way that the different effects are not correlated. == Details == Many research areas see increasingly large numbers of variables in only few samples. The low sample to variable ratio creates problems known as multicollinearity and singularity. Because of this, most traditional multivariate statistical methods cannot be applied. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「ANOVA–simultaneous component analysis」の詳細全文を読む スポンサード リンク
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