plsVarSel (version 0.9.13)

VIP: Filter methods for variable selection with Partial Least Squares.

Description

Various filter methods extracting and using information from mvr objects to assign importance to all included variables. Available methods are Significance Multivariate Correlation (sMC), Selectivity Ratio (SR), Variable Importance in Projections (VIP), Loading Weights (LW), Regression Coefficients (RC).

Usage

VIP(pls.object, opt.comp, p = dim(pls.object$coef)[1])
SR(pls.object, opt.comp, X)
sMC(pls.object, opt.comp, X, alpha_mc = 0.05)
LW(pls.object, opt.comp)
RC(pls.object, opt.comp)
URC(pls.object, opt.comp)
FRC(pls.object, opt.comp)
mRMR(pls.object, nsel, X)

Value

A vector having the same length as the number of variables in the associated PLS model. High values are associated with high importance, explained variance or relevance to the model.

The sMC has an attribute "quantile", which is the associated quantile of the F-distribution, which can be used as a cut-off for significant variables, similar to the cut-off of 1 associated with the VIP.

Arguments

pls.object

mvr object from PLS regression.

opt.comp

optimal number of components of PLS model.

p

number of variables in PLS model.

X

data matrix used as predictors in PLS modelling.

alpha_mc

quantile significance for automatic selection of variables in sMC.

nsel

number of variables to select.

Author

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

Details

From plsVarSel 0.9.10, the VIP method handles multiple responses correctly, as does the LW method. All other filter methods implemented in this package assume a single response and will give its results based on the first response in multi-response cases.

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.

Examples

data(gasoline, package = "pls")
library(pls)
pls  <- plsr(octane ~ NIR, ncomp = 10, validation = "LOO", data = gasoline)
comp <- which.min(pls$validation$PRESS)
X    <- unclass(gasoline$NIR)
vip <- VIP(pls, comp)
sr  <- SR (pls, comp, X)
smc <- sMC(pls, comp, X)
lw  <- LW (pls, comp)
rc  <- RC (pls, comp)
urc <- URC(pls, comp)
frc <- FRC(pls, comp)
mrm <- mRMR(pls, 401, X)$score
matplot(scale(cbind(vip, sr, smc, lw, rc, urc, frc, mrm)), type = 'l')