Premium
The GIFI approach to non‐linear PLS modeling
Author(s) -
Berglund Anders,
Kettaneh Nouna,
Uppgård LiseLott,
Wold Svante,
Bendwell Nancy,
Cameron Dave R.
Publication year - 2001
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.679
Subject(s) - principal component analysis , linear regression , coding (social sciences) , principal component regression , computer science , set (abstract data type) , regression , regression analysis , data set , transformation (genetics) , linear model , mathematics , data mining , statistics , artificial intelligence , machine learning , chemistry , biochemistry , gene , programming language
The GIFI approach to non‐linear modeling involves the transformation of quantitative variables to a set of 1/0 dummies in a similar manner to the way qualitative variables are coded. This is followed by analyzing the sets of 1/0 dummies by principal component analysis, multiple regression or, as discussed here, PLS. The patterns of the resulting coefficients indicate the nature of the non‐linearities in the data. Here the potential uses and limitations of PLS regression, in combination with four variants of GIFI coding, are investigated using both simulated and empirical data sets. Copyright © 2001 John Wiley & Sons, Ltd.