select.sgpls {sgPLS}

Output of selected variables from a gPLS model or a sgPLS model

Description

This function outputs the selected variables on each component for the group and sparse group PLS.

Usage

  select.sgpls(model)

Arguments

model

object of class inheriting from "gPLS" or"sgPLS".

Value

select.sgpls produces a list with the following components:

group.size.X

A matrix containing in the first column the size of the groups in the X dataset. Then, the next columns indicate the size of the groups selected for each component.

select.group.X

A list containing for each element (corresponding to each group of the X dataset) the indices of the variables selected.

group.size.Y

A matrix containing in the first column the size of the groups in the Y dataset. Then the next columns indicate the size of the groups selected for each component.

select.group.Y

A list containing for each element (corresponding to each group of the Y dataset) the indices of the variables selected.

select.X

A list containing for each element (corresponding to each component of the gPLS or sgPLS model) the names of the selected variables in the X dataset.

select.Y

A list containing for each element (corresponding to each component of the gPLS or sgPLS model) the names of the selected variables in the Y dataset.

select.X.total

The names of the variables selected from the gPLS or sgPLS model regarding the X matrix.

select.Y.total

The names of the variables selected from the gPLS or sgPLS model regarding the Y matrix.

Author(s)

Benoit Liquet, [e-mail],
Pierre Lafaye de Micheaux [e-mail]

Examples

## Not run: 	
## Simulation of datasets X and Y with group variables
n <- 100
sigma.gamma <- 1
sigma.e <- 1.5
p <- 400
q <- 500
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))

theta.y1 <- 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(0,425))
theta.y2 <- c(rep(0,420),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))                            


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

set.seed(125)

gam1 <- rnorm(n)
gam2 <- rnorm(n)

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) %*% matrix(c(theta.y1, theta.y2),
     nrow = 2, byrow = TRUE) + rmvnorm(n, mean = rep(0, q), sigma =
     Sigmay, method = "svd")

ind.block.x <- seq(20, 380, 20)
ind.block.y <- seq(20, 480, 20)

#### gPLS model
model.sgPLS <- sgPLS(X, Y, ncomp = 2, mode = "regression", keepX = c(4, 4), 
                   keepY = c(4, 4), ind.block.x = ind.block.x,
                   ind.block.y = ind.block.y,
                   alpha.x = c(0.5, 0.5), alpha.y = c(0.5, 0.5))

result.sgPLS <- select.sgpls(model.sgPLS)
result.sgPLS$group.size.X
result.sgPLS$group.size.Y

#### gPLS model
model.gPLS <- gPLS(X, Y, ncomp = 2, mode = "regression", keepX = c(4, 4), 
     keepY = c(4,4), ind.block.x = ind.block.x ,ind.block.y = ind.block.y)

result.gPLS <- select.sgpls(model.gPLS)
result.gPLS$group.size.X
result.gPLS$group.size.Y




## End(Not run)