tuning.gPLS.X: Choice of the tuning parameter (number of groups) related to...

In sgPLS: Sparse Group Partial Least Square Methods

Choice of the tuning parameter (number of groups) related to predictor matrix for gPLS model (regression mode)

Description

For a grid of tuning parameter, this function computes by leave-one-out or M-fold cross-validation the MSEP (Mean Square Error of Prediction) of a gPLS model.

Usage

tuning.gPLS.X(X,Y,folds=10,validation=c("Mfold","loo"),
		ncomp,keepX=NULL,grid.X,setseed,progressBar=FALSE,
		ind.block.x=ind.block.x)

Arguments

X	Numeric matrix or data frame (n \times p), the observations on the X variables.
Y	Numeric matrix or data frame (n \times q), the observations on the Y variables.
folds	Positive integer. Number of folds to use if validation="Mfold". Defaults to folds=10.
validation	Character string. What kind of (internal) cross-validation method to use, (partially) matching one of "Mfolds" (M-folds) or "loo" (leave-one-out).
ncomp	Number of component for investigating the choice of the tuning parameter.
keepX	Vector of integer indicating the number of group of variables to keep in each component. See details for more information.
grid.X	Vector of integers defining the values of the tuning parameter (corresponding to the number of group of variables to select) at which cross-validation score should be computed.
setseed	Integer indicating the random number generation state.
progressBar	By default set to FALSE to output the progress bar of the computation.
ind.block.x	A vector of integers describing the grouping of the X variables. (see an example in details section)

Details

If validation="Mfolds", M-fold cross-validation is performed by calling Mfold. The folds are generated. The number of cross-validation folds is specified with the argument folds.

If validation="loo", leave-one-out cross-validation is performed by calling the loo function. In this case the arguments folds are ignored.

if keepX is specified (by default is NULL), each element of keepX indicates the value of the tuning parameter for the corresponding component. Only the choice of the tuning parameters corresponding to the remaining components are investigating by evaluating the cross-validation score at different values defining by grid.X.

Value

The returned value is a list with components:

MSEP	Matrix containing the cross-validation score computed on the grid.
keepX	Value of the tuning parameter (lambda) on which the cross-validation method reached it minimum.

Author(s)

Benoit Liquet and Pierre Lafaye de Micheaux

Examples

## Not run: 	
## Simulation of Datasets X (with group variables) and Y a multivariate response variable 
n <- 200
sigma.e <- 0.5
p <- 400
q <- 10
theta.x1 <- c(rep(1,15),rep(0,5),rep(-1,15),rep(0,5),rep(1.5,15),
			rep(0,5),rep(-1.5,15),rep(0,325))
theta.x2 <- c(rep(0,320),rep(1,15),rep(0,5),rep(-1,15),rep(0,5),
			rep(1.5,15),rep(0,5),rep(-1.5,15),rep(0,5))

set.seed(125)
theta.y1 <- runif(10,0.5,2)
theta.y2 <- runif(10,0.5,2)
  
temp <-  matrix(c(theta.y1,theta.y2),nrow=2,byrow=TRUE)

Sigmax <- matrix(0,nrow=p,ncol=p)
diag(Sigmax) <- sigma.e^2
Sigmay <- matrix(0,nrow=q,ncol=q)
diag(Sigmay) <- sigma.e^2

gam1 <- rnorm(n,0,1)
gam2 <- rnorm(n,0,1)

X <- matrix(c(gam1,gam2),ncol=2,byrow=FALSE)%*%matrix(c(theta.x1,theta.x2),nrow=2,byrow=TRUE)
+rmvnorm(n,mean=rep(0,p),sigma=Sigmax,method="svd")
Y <- matrix(c(gam1,gam2),ncol=2,byrow=FALSE)%*%t(svd(temp)$v)
+rmvnorm(n,mean=rep(0,q),sigma=Sigmay,method="svd")

ind.block.x <- seq(20,380,20)

grid.X <- 1:16

## Strategy with same value for both components
tun.gPLS <- tuning.gPLS.X(X, Y, folds = 10, validation = c("Mfold", "loo"), 
		ncomp=2,keepX = NULL, grid.X=grid.X, setseed=1, progressBar = FALSE, 
		ind.block.x = ind.block.x) 

tun.gPLS$keepX # for each component

##For a sequential strategy
tun.gPLS.1 <- tuning.gPLS.X(X, Y, folds = 10, validation = c("Mfold", "loo"),
			 ncomp=1, keepX = NULL, grid.X=grid.X, setseed=1,
                             ind.block.x = ind.block.x) 
tun.gPLS.1$keepX # for the first component

tun.gPLS.2 <- tuning.gPLS.X(X, Y, folds = 10, validation = c("Mfold", "loo"), ncomp=2, 
                            keepX = tun.gPLS.1$keepX , grid.X=grid.X, setseed=1, 
                            ind.block.x = ind.block.x) 

tun.gPLS.2$keepX # for the second component

## End(Not run)

sgPLS documentation built on Oct. 5, 2023, 5:06 p.m.