covsel: CovSel

In rchemo: Dimension Reduction, Regression and Discrimination for Chemometrics

CovSel

Description

Variable selection for high-dimensionnal data with the COVSEL method (Roger et al. 2011).

Usage

covsel(X, Y, nvar = NULL, Xscaling = c("none", "pareto", "sd")[1], 
Yscaling = c("none", "pareto", "sd")[1], weights = NULL)

Arguments

X	X-data (n, p).
Y	Y-data (n, q).
nvar	Number of variables to select in X.
Xscaling	X variable scaling among "none" (mean-centering only), "pareto" (mean-centering and pareto scaling), "sd" (mean-centering and unit variance scaling). If "pareto" or "sd", uncorrected standard deviation is used.
Yscaling	Y variable scaling among "none" (mean-centering only), "pareto" (mean-centering and pareto scaling), "sd" (mean-centering and unit variance scaling). If "pareto" or "sd", uncorrected standard deviation is used.
weights	Weights (n, 1) to apply to the training observations. Internally, weights are "normalized" to sum to 1. Default to NULL (weights are set to 1 / n).

Value

sel	A dataframe where variable sel shows the column numbers of the variables selected in X.
weights	The weights used for the row observations.

References

Roger, J.M., Palagos, B., Bertrand, D., Fernandez-Ahumada, E., 2011. CovSel: Variable selection for highly multivariate and multi-response calibration: Application to IR spectroscopy. Chem. Lab. Int. Syst. 106, 216-223.

Examples

n <- 6 ; p <- 4
X <- matrix(rnorm(n * p), ncol = p)
Y <- matrix(rnorm(n * 2), ncol = 2)

covsel(X, Y, nvar = 3)

rchemo documentation built on June 30, 2026, 5:10 p.m.