Optimised score plot by principal components of predictions
Publication details
Journal : Chemometrics and Intelligent Laboratory Systems , vol. 68 , p. 61–74 , 2003
Publisher : Elsevier
International Standard Numbers
:
Printed
:
0169-7439
Electronic
:
1873-3239
Publication type : Academic article
Issue : 1-2
Links
:
DOI
:
doi.org/10.1016/S0169-7439(03)...
If you have questions about the publication, you may contact Nofima’s Chief Librarian.
Kjetil Aune
Chief Librarian
kjetil.aune@nofima.no
Summary
A common problem in statistics/chemometrics is to relate two data matrices (X and Y) to each other, with the purpose of either prediction or interpretation. Usually, one is interested in understanding which directions in Y-space that can be predicted by which directions in X-space. Several methods exist for this, for instance, PLS regression and canonical correlation. The present paper presents a new plot for visualising the relationship between X and Y. The plot is based on a decomposition of the X-space that is optimal with respect to Y-variance. The new procedure can accompany any regression method. (C) 2003 Elsevier Science B.V. All rights reserved.