Description Usage Arguments Value Author(s) Examples
Gives the value of the penalty parameters (lambdaX,lambdaY) having the minimum RMSEP value.
1 2 | 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, ...)
|
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 |
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
Opeoluwa F. Oyedele and Sugnet Gardner-Lubbe
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | 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
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.