Improving the detection of significant factors using ANOVA-PCA by selective reduction of residual variability

• Type: Article in International Scientific Journal
• Date: 2009

Authors

Climaco Pinto, R (Author) Other; Barros, AS (Author) Other; Locquet, N (Author) Other; Schmidtke, L (Author) Other; Rutledge, DN (Author) Other

Journal

Title: Analytica Chimica Acta

Vol. 653

Pages: 131-142

ISSN: 0003-2670

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 24 Citations

Scopus - 28 Citations

Other information

Authenticus ID: P-003-EZN

DOI: 10.1016/j.aca.2009.09.016

Abstract (EN): Selective elimination of residual error can be used when applying Harrington's ANOVA-PCA in order to improve the capabilities of the method. ANOVA-PCA is sometimes unable to discriminate between levels of a factor when sources of high residual variability are present. In some cases this variability is not random, possesses some structure and is large enough to be responsible for the first principal components calculated by the PCA step in the ANOVA-PCA. This fact sometimes makes it impossible for the interesting variance to be in the first two PCA components. By using the proposed selective residuals elimination procedure, one may improve the ability of the method to detect significant factors as well as have an understanding of the different kinds of residual variance present in the data. Two datasets are used to show how the method is used in order to iteratively detect variance associated with the factors even when it is not initially visible. A permutation method is used to confirm that the observed significance of the factors was not accidental.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12