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")
install.packages("plsVarSel", repos = c('https://khliland.r-universe.dev', 'https://cloud.r-project.org'))

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.