PLSbiplot1 (version 0.1)

mod.SPLS: Sparse Partial Least Squares (SPLS) algorithm

Description

Takes in a set of predictor variables and a set of response variables and gives the SPLS parameters.

Usage

mod.SPLS(X, Y, A, lambdaY, lambdaX, eps, ...)

Arguments

X

A (NxP) predictor matrix

Y

A (NxM) response matrix

A

The number of PLS components

lambdaY

A value for the penalty parameters for the soft-thresholding penalization function for Y-weights

lambdaX

A value for the penalty parameters for the soft-thresholding penalization function for X-weights

eps

Cut off value for convergence step

...

Other arguments. Currently ignored

Value

The SPLS parameters of D=[X Y]

Examples

if(require(chemometrics))
data(ash, package="chemometrics")
X1 = as.matrix(ash[,10:17], ncol=8)
Y1 = as.matrix(ash$SOT)
colnames(Y1) = paste("SOT")
mod.SPLS(X=scale(X1), Y=scale(Y1), A=2, lambdaY=0, lambdaX=10.10, eps=1e-5)
#lambdaX and lambdaY value are determined using function opt.penalty.values
#for more details, see opt.penalty.values help file