PLSbiplot1 (version 0.1)

mod.NIPALS: The Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm

Description

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

Usage

mod.NIPALS(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 NIPALS algorithm

Examples

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