T2_pls: Hotelling's T^2 based variable selection in PLS - T^2-PLS)

In plsVarSel: Variable Selection in Partial Least Squares

Hotelling's T^2 based variable selection in PLS – T^2-PLS)

Description

Variable selection based on the T^2 statistic. A side effect of running the selection is printing of tables and production of plots.

Usage

T2_pls(ytr, Xtr, yts, Xts, ncomp = 10, alpha = c(0.2, 0.15, 0.1, 0.05, 0.01))

Arguments

ytr	Vector of responses for model training.
Xtr	Matrix of predictors for model training.
yts	Vector of responses for model testing.
Xts	Matrix of predictors for model testing.
ncomp	Number of PLS components.
alpha	Hotelling's T^2 significance levels.

Value

Parameters and variables corresponding to variable selections of minimum error and minimum variable set.

References

Tahir Mehmood, Hotelling T^2 based variable selection in partial least squares regression, Chemometrics and Intelligent Laboratory Systems 154 (2016), pp 23-28

Examples

data(gasoline, package = "pls")
library(pls)
if(interactive()){
  t2 <- T2_pls(gasoline$octane[1:40], gasoline$NIR[1:40,], 
             gasoline$octane[-(1:40)], gasoline$NIR[-(1:40),], 
             ncomp = 10, alpha = c(0.2, 0.15, 0.1, 0.05, 0.01))
  matplot(t(gasoline$NIR), type = 'l', col=1, ylab='intensity')
  points(t2$mv[[1]], colMeans(gasoline$NIR)[t2$mv[[1]]], col=2, pch='x')
  points(t2$mv[[2]], colMeans(gasoline$NIR)[t2$mv[[2]]], col=3, pch='o')
}

plsVarSel documentation built on May 29, 2024, 7:34 a.m.