bve_pls: Backward variable elimination PLS (BVE-PLS)

In khliland/plsVarSel: Variable Selection in Partial Least Squares

Backward variable elimination PLS (BVE-PLS)

Description

A backward variable elimination procedure for elimination of non informative variables.

Usage

bve_pls(y, X, ncomp = 10, ratio = 0.75, VIP.threshold = 1)

Arguments

y	vector of response values (numeric or factor).
X	numeric predictor matrix.
ncomp	integer number of components (default = 10).
ratio	the proportion of the samples to use for calibration (default = 0.75).
VIP.threshold	thresholding to remove non-important variables (default = 1).

Details

Variables are first sorted with respect to some importancemeasure, and usually one of the filter measures described above are used. Secondly, a threshold is used to eliminate a subset of the least informative variables. Then a model is fitted again to the remaining variables and performance is measured. The procedure is repeated until maximum model performance is achieved.

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

I. Frank, Intermediate least squares regression method, Chemometrics and Intelligent Laboratory Systems 1 (3) (1987) 233-242.

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, bve_pls(octane, NIR) )

khliland/plsVarSel documentation built on Feb. 5, 2023, 3:15 a.m.