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

In PLSbiplot1: The Partial Least Squares (PLS) Biplot

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]

Author(s)

Opeoluwa F. Oyedele and Sugnet Gardner-Lubbe

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

PLSbiplot1 documentation built on May 2, 2019, 9:41 a.m.