Abstract
It is still widely unknown in chemometrics that the statistical analysis of compositional data requires fundamentally different tools than a similar analysis of unconstrained data. This article examines the problems that potentially occur when one performs a partial least squares (PLS) analysis on compositional data and suggests logcontrast partial least squares (LCPLS) as an alternative.
| Original language | English |
|---|---|
| Pages (from-to) | 159-172 |
| Number of pages | 14 |
| Journal | Chemometrics and Intelligent Laboratory Systems |
| Volume | 30 |
| Issue number | 1 |
| DOIs | |
| State | Published - Nov 1995 |
Bibliographical note
Funding Information:During the course of this research Professor Rayens was supported by NSF grant ATM-9108177.
Funding
During the course of this research Professor Rayens was supported by NSF grant ATM-9108177.
| Funders | Funder number |
|---|---|
| National Science Foundation (NSF) | ATM-9108177 |
Keywords
- Compositional data
- Partial least squares
ASJC Scopus subject areas
- Software
- Analytical Chemistry
- Process Chemistry and Technology
- Spectroscopy
- Computer Science Applications