# Output of selected variables from a gPLS model or a sgPLS model This function outputs the selected variables on each component for the group and sparse group PLS. ```r select.sgpls(model) ``` ## Arguments - `model`: object of class inheriting from `"gPLS"` or`"sgPLS"`. ## Returns `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, b.liquet@uq.edu.au , Pierre Lafaye de Micheaux lafaye@dms.umontreal.ca ## Examples ```r ## 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) ```

select.sgpls function