T2_pls | R Documentation |
Variable selection based on the T^2 statistic. A side effect
of running the selection is printing of tables and production of plots,
as the T^2 calculations done by mult.chart
.
T2_pls(ytr, Xtr, yts, Xts, ncomp = 10, alpha = c(0.2, 0.15, 0.1, 0.05, 0.01))
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. |
Parameters and variables corresponding to variable selections of minimum error and minimum variable set.
Tahir Mehmood, Hotelling T2 based variable selection in partial least squares regression, Chemometrics and Intelligent Laboratory Systems 154 (2016), pp 23-28
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') }
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