plsVarSel (version 0.9.12)

rep_pls: 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)

Value

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

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).

Author

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

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.

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")
if (FALSE) {
with( gasoline, rep_pls(octane, NIR) )
}