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Scatter plotting in multivariate data analysis
Author(s) -
Geladi Paul,
Manley Marena,
Lestander Torbjörn
Publication year - 2003
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.814
Subject(s) - biplot , chemometrics , principal component analysis , multivariate statistics , partial least squares regression , visualization , computer science , regression , statistics , mathematics , artificial intelligence , chemistry , machine learning , biochemistry , genotype , gene
In data analysis, many situations arise where plotting and visualization are helpful or an absolute requirement for understanding. There are many techniques of plotting data/parameters/residuals. These have to be understood and visualization has to be made clearly and interpreted correctly. In this paper the classical favourites in chemometrics, scatter plots, are looked into more deeply and some criticism based on recent literature references is formulated for situations of principal component analysis, PARAFAC three‐way analysis and regression by partial least squares. Biplots are also afforded some attention. Examples from near‐infrared spectroscopy are given as illustrations. Copyright © 2003 John Wiley & Sons, Ltd.