select.spls: Output of selected variables from a sPLS model

In sgPLS: Sparse Group Partial Least Square Methods

Output of selected variables from a sPLS model

Description

This function outputs the selected variables on each component for the sPLS.

Usage

  select.spls(model)

Arguments

model

object of class inheriting from "sPLS".

Value

select.spls produces a list with the following components:

select.X	A list containing for each element (corresponding to each component of the sPLS 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 sPLS model) the names of the selected variables in the Y dataset.
select.X.total	The names of the variables selected from the sPLS model regarding the X matrix.
select.Y.total	The names of the variables selected from the sPLS 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))                             

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

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)

#### sPLS model
model.sPLS <- sPLS(X, Y, ncomp = 2, mode = "regression", keepX = c(60, 60), 
                     keepY = c(60, 60))
result.sPLS <- select.spls(model.sPLS)
result.sPLS$select.X
result.sPLS$select.Y




## End(Not run)

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