Filter methods for variable selection with Partial Least Squares.

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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

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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)

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.

Value

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

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

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.

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

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