covSel: Covariance Selection - CovSel

In khliland/plsVarSel: Variable Selection in Partial Least Squares

Covariance Selection - CovSel

Description

Sequential selection of variables based on squared covariance with response and intermediate deflation (as in Partial Least Squares).

Usage

covSel(X, Y, nvar)

Arguments

X	matrix of input variables
Y	matrix of response variable(s)
nvar	maximum number of variables

Value

selected	an integer vector of selected variables
scores	a matrix of score vectors
loadings	a matrix of loading vectors
Yloadings	a matrix of Y loadings

References

J.M. Roger, B. Palagos, D. Bertrand, E. Fernandez-Ahumada. CovSel: Variable selection for highly multivariate and multi-response calibration: Application to IR spectroscopy. Chemom Intel Lab Syst. 2011;106(2):216-223. P. Mishra, A brief note on a new faster covariate's selection (fCovSel) algorithm, Journal of Chemometrics 36(5) 2022.

Examples

data(gasoline, package = "pls")
sels <- with(gasoline, covSel(NIR, octane, 5))
matplot(t(gasoline$NIR), type = "l")
abline(v = sels$selected, col = 2)

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