opt.penalty.values: Choosing a value for penalty parameters lambdaX and lambdaY...

In PLSbiplot1: The Partial Least Squares (PLS) Biplot

Description

Gives the value of the penalty parameters (lambdaX,lambdaY) having the minimum RMSEP value.

Usage

opt.penalty.values(X, Y, A, algorithm = NULL, eps, from.value.X, to.value.X,
  from.value.Y, to.value.Y, lambdaY.len, lambdaX.len, ...)

Arguments

X	A (NxP) predictor matrix
Y	A (NxM) response matrix; can also be a vector in the case of the SPLS-GLM
A	The number of Partial Least Squares (PLS) components
algorithm	Any of the SPLS or SPLS-GLM algorithms ("mod.SPLS", "SPLS.GLM", "SPLS.binomial.GLM")
eps	Cut off value for convergence step
from.value.X	starting value for lambdaX
to.value.X	ending value for lambdaX
from.value.Y	starting value for lambdaY
to.value.Y	ending value for lambdaY
lambdaY.len	length of lambdaY value
lambdaX.len	length of lambdaX value
...	Other arguments. Currently ignored

Value

the value of the penalty parameters (lambdaX,lambdaY) having the minimum RMSEP value, as well as the RMSEP values obtained when the lambdaX and lambdaY values were paired together

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")
#choosing a value for the penalty parameters lambdaY and lambdaX for this data
opt.penalty.values(X=scale(X1), Y=scale(Y1), A=2, algorithm=mod.SPLS, eps=1e-5,
from.value.X=0, to.value.X=500, from.value.Y=0, to.value.Y=0, lambdaY.len=1, lambdaX.len=100)
#thus, use lambdaX = 10.10 and lambdaY = 0 for the SPLS analysis of this data

#possum.mat data
if(require(robustbase))
possum.mat
y = as.matrix(possum.mat[,1], ncol=1)
dimnames(y) = list(paste("S", 1:nrow(possum.mat), seq=""), "Diversity")
X = as.matrix(possum.mat[,2:14], ncol=13)
dimnames(X) = list(paste("S", 1:nrow(possum.mat), seq=""), colnames(possum.mat[,2:14]))
#choosing a value for the penalty parameters lambdaY and lambdaX for this data
opt.penalty.values(X=scale(X), Y=scale(y), A=2, algorithm=SPLS.GLM, eps=1e-3,
from.value.X=1, to.value.X=4, from.value.Y=0, to.value.Y=0, lambdaY.len=1, lambdaX.len=100)
#thus, use lambdaY = 0 and lambdaX = 3.3 for the (Poisson) SPLS-GLM analysis of this data

#Pima.tr data
if(require(MASS))
data(Pima.tr, package="MASS")
X = as.matrix(cbind(Pima.tr[,1:7]))
dimnames(X) = list(1:nrow(X), colnames(X))
y = as.matrix(as.numeric(Pima.tr$type)-1, ncol=1)
#0=No and 1=Yes
dimnames(y) = list(1:nrow(y), paste("type"))
#choosing a value for the penalty parameters lambdaY and lambdaX for this data
opt.penalty.values(X=scale(X), Y=scale(y), A=2, algorithm=SPLS.binomial.GLM, eps=1e-3,
from.value.X=0, to.value.X=95, from.value.Y=0, to.value.Y=0, lambdaY.len=1, lambdaX.len=100)
#thus, use lambdaY = 0 and lambdaX = 0.96 for the (Binomial) SPLS-GLM analysis of this data

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