Abstract

Abstract 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.

Keywords

Principal component analysisLinear regressionCoding (social sciences)Principal component regressionComputer scienceSet (abstract data type)RegressionRegression analysisData setTransformation (genetics)Linear modelMathematicsData miningStatisticsArtificial intelligenceMachine learningChemistry

Affiliated Institutions

Related Publications

Publication Info

Year
2001
Type
article
Volume
15
Issue
4
Pages
321-336
Citations
36
Access
Closed

External Links

View on DOI.org

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

36
OpenAlex

Cite This

Anders Berglund, Nouna Kettaneh, Lise‐Lott Uppgård et al. (2001). The GIFI approach to non‐linear PLS modeling. Journal of Chemometrics , 15 (4) , 321-336. https://doi.org/10.1002/cem.679

Identifiers

DOI
10.1002/cem.679