## Abstract Selection of the number of latent variables (LVs) to include in a partial least squares (PLS) model is an important step in the data analysis. Inclusion of too few or too many LVs may lead to, respectively, under or overβfitting of the data and subsequently result in poor future model p
Regression coefficients in multilinear PLS
β Scribed by Sijmen de Jong
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 108 KB
- Volume
- 12
- Category
- Article
- ISSN
- 0886-9383
No coin nor oath required. For personal study only.
β¦ Synopsis
Three alternative approaches are discussed for finding the final calibration model (regression coefficients) in PLS regression of k-way Y on N-way X. The simplest approach is to skip the deflation of the X-data. From the observation that the specific deflation used in multiway PLS is inconsequential, it also follows that Bro's tri-PLS is equivalent to StΓ₯hle's linear three-way decomposition (LTD).
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## Abstract Shapley value regression consists of assessing relative importance and accordingly adjusting regression coefficients. It is argued that adjustment of coefficients is unnecessary and even misleading for practically relevant situations. Examples are given, and an alternative procedure is