mcuve_pls: Uninformative variable elimination in PLS (UVE-PLS)

In khliland/plsVarSel: Variable Selection in Partial Least Squares

Uninformative variable elimination in PLS (UVE-PLS)

Description

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.

Usage

mcuve_pls(y, X, ncomp = 10, N = 3, ratio = 0.75, MCUVE.threshold = NA)

Arguments

y	vector of response values (numeric or factor).
X	numeric predictor matrix.
ncomp	integer number of components (default = 10).
N	number of samples Mone Carlo simulations (default = 3).
ratio	the proportion of the samples to use for calibration (default = 0.75).
MCUVE.threshold	thresholding separate signal from noise (default = NA creates automatic threshold from data).

Value

Returns a vector of variable numbers corresponding to the model having lowest prediction error.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

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.

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")
with( gasoline, mcuve_pls(octane, NIR) )

khliland/plsVarSel documentation built on April 24, 2024, 11:21 a.m.