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
1 2 3 4 5 6 7 8 9 
Arguments
pls.object 

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 
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) 6269.
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
1 2 3 4 5 6 7 8 9 10 11  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')
