Variable Selection in Partial Least Squares

Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported.

Variable selection methods for Partial Least Squares - plsVarSel

Installation

# Install release version from CRAN  
install.packages("plsVarSel")  
# Install development version from GitHub  
devtools::install_github("khliland/plsVarSel")

Contents

• Filter methods
  • VIP - Variable Importance in Projections
  • SR - Selectivity Ratio
  • sMC - Significance Multivariate Correlation
  • LW - Loading Weights
  • RC - Regression Coefficients
  • URC - RC scaled as abs(RC)/max(abs(RC))
  • FRC - URC further scaled as URC/PRESS
  • mRMR - Minimum Redundancy Maximal Relevancy
• Wrapper methods
  • BVE-PLS - Backward variable elimination PLS
  • GA-PLS - Genetic algorithm combined with PLS regression
  • IPW-PLS - Iterative predictor weighting PLS
  • MCUVE-PLS - Uninformative variable elimination in PLS
  • REP-PLS - Regularized elimination procedure in PLS
  • SPA-PLS - Sub-window permutation analysis coupled with PLS
  • T2-PLS - Hotelling's T^2 based variable selection in PLS
  • WVC-PLS - Weighted Variable Contribution in PLS
• Embedded methods
  • Trunction PLS
  • ST-PLS - Soft-Threshold PLS
  • CovSel - Covariance Selection
  • PVS/PVR - Principal Variable Selection and Regression
• LDA wrappers for PLS classficiations and cross-validation
• Shaving - Repeated shaving of variables using filters (experimental)
• Simulation tools

Main references (more in package)

• T. Mehmood, K.H. Liland, L. Snipen, S. Sæbø, A review of variable selection methods in Partial Least Squares Regression, Chemometrics and Intelligent Laboratory Systems 118 (2012) 62-69.
• T. Mehmood, S. Sæbø, K.H. Liland, Comparison of variable selection methods in partial least squares regression, Journal of Chemometrics 34 (2020) e3226.