plsVarSel: Variable selection in Partial Least Squares

In plsVarSel: Variable Selection in Partial Least Squares

Variable selection in Partial Least Squares

Description

A large collection of variable selection methods for use with Partial Least Squares. These include all methods in Mehmood et al. 2012 and more. All functions treat numeric responses as regression and factor responses as classification. Default classification is PLS + LDA, but setDA() can be used to choose PLS + QDA or PLS with response column maximization.

References

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.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

plsVarSel documentation built on Jan. 12, 2023, 5:09 p.m.