rep_pls: Regularized elimination procedure in PLS

In plsVarSel: Variable Selection in Partial Least Squares

Regularized elimination procedure in PLS

Description

A regularized variable elimination procedure for parsimonious variable selection, where also a stepwise elimination is carried out

Usage

rep_pls(y, X, ncomp = 5, ratio = 0.75, VIP.threshold = 0.5, N = 3)

Arguments

y	vector of response values (numeric or factor).
X	numeric predictor matrix.
ncomp	integer number of components (default = 5).
ratio	the proportion of the samples to use for calibration (default = 0.75).
VIP.threshold	thresholding to remove non-important variables (default = 0.5).
N	number of samples in the selection matrix (default = 3).

Details

A stability based variable selection procedure is adopted, where the samples have been split randomly into a predefined number of training and test sets. For each split, g, the following stepwise procedure is adopted to select the variables.

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

T. Mehmood, H. Martens, S. Sæbø, J. Warringer, L. Snipen, A partial least squares based algorithm for parsimonious variable selection, Algorithms for Molecular Biology 6 (2011).

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")
## Not run: 
with( gasoline, rep_pls(octane, NIR) )

## End(Not run)

plsVarSel documentation built on Jan. 12, 2023, 5:09 p.m.