plsVarSel (version 0.10.0)

PVS: Principal Variable Selection (PVS)

Description

Greedy algorithm for extracting the most dominant (principal) variables (X-columns) with respect to explained X-variance.

Usage

PVS(X, nvar, ncomp = NULL)

Value

A list containing:

Q

Orthonormal scores (associated with the selected variables).

R

Corresponding loadings. NOTE: R[,vperm] is upper triangular.

ids

Indices arranged in the order of the nvar selected variables.

vperm

Indices arranged in the order of the nvar selected and all non-selected variables. NOTE: R[,vperm] is upper triangular.

ssEX

The variances explained by the selected variables.

ni

The norms of the (residual) selected variables before the score-normalization (Q).

U

The normalized PCA-scores.

s

Singular values of the mean centered X.

Arguments

X

numeric predictor matrix.

nvar

integer, the required number of selected variables.

ncomp

integer, number of principal components included in the voting (default = all PCs).

Author

Ulf Indahl, Kristian Hovde Liland.

References

Joakim Skogholt, Kristian Hovde Liland, Tormod Næs, Age K. Smilde, Ulf Geir Indahl, Selection of principal variables through a modified Gram–Schmidt process with and without supervision, Journal of Chemometrics, Volume 37, Issue 10, Pages e3510 (2023), https://doi.org/10.1002/cem.3510

See Also

PVR, VIP, filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls.

Examples

library(pls)
data(gasoline, package = "pls")

# PVS: Select 10 variables using all PCs in voting
pvs_result <- PVS(gasoline$NIR, nvar = 10)

# Compare with PCA using pcr() (octane is unused in PCA)
pca_result <- pcr(octane ~ NIR, ncomp = 10, data = gasoline, scale = FALSE)

# Plot cumulative variance explained
plot(cumsum(pvs_result$ssEX), type = "b", col = "blue", 
     xlab = "Number of Variables/Components", 
     ylab = "Cumulative % Variance Explained",
     main = "PVS vs PCA", ylim = c(0, 100))
pca_var <- 100 * cumsum(pca_result$Xvar) / pca_result$Xtotvar
lines(seq_along(pca_var), pca_var, type = "b", col = "red")
legend("bottomright", legend = c("PVS (10 variables)", "PCA (10 components)"),
       col = c("blue", "red"), lty = 1, pch = 1)