The Kernel algorithm for few(er) samples but large variables by Rannar et al. (1994)

Description

Takes in a set of predictor variables and a set of response variables and gives the Partial Least Squares (PLS) parameters.

Usage

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mod.KernelPLS_R(X, Y, A, ...)

Arguments

X

A (NxP) predictor matrix

Y

A (NxM) response matrix

A

The number of PLS components

...

Other arguments. Currently ignored

Value

The PLS parameters using the Kernel algorithm by RC$nnar et al. (1994)

Author(s)

Opeoluwa F. Oyedele and Sugnet Gardner-Lubbe

Examples

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if(require(pls))
data(oliveoil, package="pls")
X = as.matrix(oliveoil$chemical, ncol=5)
dimnames(X) = list(paste(c("G1","G2","G3","G4","G5","I1","I2","I3","I4","I5",
"S1","S2","S3","S4","S5","S6")),
paste(c("Acidity","Peroxide","K232","K270","DK")))
Y = as.matrix(oliveoil$sensory, ncol=6)
dimnames(Y) = list(paste(c("G1","G2","G3","G4","G5","I1","I2","I3","I4","I5",
"S1","S2","S3","S4","S5","S6")),
paste(c("Yellow","Green","Brown","Glossy","Transp","Syrup")))
mod.KernelPLS_R(X, Y, A=5)

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