Artificial noise variables are added to the predictor set before the PLSR model is fitted. All the original variables having lower "importance" than the artificial noise variables are eliminated before the procedure is repeated until a stop criterion is reached.
mcuve_pls(y, X, ncomp = 10, N = 3, ratio = 0.75, MCUVE.threshold = NA)Returns a vector of variable numbers corresponding to the model having lowest prediction error.
vector of response values (numeric or factor).
numeric predictor matrix.
integer number of components (default = 10).
number of samples Mone Carlo simulations (default = 3).
the proportion of the samples to use for calibration (default = 0.75).
thresholding separate signal from noise (default = NA creates automatic threshold from data).
Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.
V. Centner, D. Massart, O. de Noord, S. de Jong, B. Vandeginste, C. Sterna, Elimination of uninformative variables for multivariate calibration, Analytical Chemistry 68 (1996) 3851-3858.
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.
data(gasoline, package = "pls")
with( gasoline, mcuve_pls(octane, NIR) )
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