bve_pls: Backward variable elimination PLS (BVE-PLS)

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/BVE.R

Description

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

Usage

1
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 S<c3><a6>b<c3><b8>.

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

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data(gasoline, package = "pls")
with( gasoline, bve_pls(octane, NIR) )

plsVarSel documentation built on May 30, 2017, 2:05 a.m.