# Group Partial Least Squares (gPLS) Function to perform group Partial Least Squares (gPLS) in the context of two datasets which are both divided into groups of variables. The gPLS approach aims to select only a few groups of variables from one dataset which are linearly related to a few groups of variables of the second dataset. latin1 ```r gPLS(X, Y, ncomp, mode = "regression", max.iter = 500, tol = 1e-06, keepX, keepY = NULL, ind.block.x, ind.block.y = NULL,scale=TRUE) ``` ## Arguments - `X`: numeric matrix of predictors. - `Y`: numeric vector or matrix of responses (for multi-response models). - `ncomp`: the number of components to include in the model (see Details). - `mode`: character string. What type of algorithm to use, (partially) matching one of `"regression"` or `"canonical"`. See Details. - `max.iter`: integer, the maximum number of iterations. - `tol`: a positive real, the tolerance used in the iterative algorithm. - `keepX`: numeric vector of length `ncomp`, the number of variables to keep in $X$-loadings. By default all variables are kept in the model. - `keepY`: numeric vector of length `ncomp`, the number of variables to keep in $Y$-loadings. By default all variables are kept in the model. - `ind.block.x`: a vector of integers describing the grouping of the $X$-variables. (see an example in Details section) - `ind.block.y`: a vector of consecutive integers describing the grouping of the $Y$-variables (see an example in Details section) - `scale`: a logical indicating if the orignal data set need to be scaled. By default `scale`=TRUE ## Details `gPLS` function fits gPLS models with $1, \ldots ,$`ncomp` components. Multi-response models are fully supported. The type of algorithm to use is specified with the `mode` argument. Two gPLS algorithms are available: gPLS regression `("regression")` and gPLS canonical analysis `("canonical")` (see References). `ind.block.x <- c(3,10,15)` means that $X$ is structured into 4 groups: X1 to X3; X4 to X10, X11 to X15 and X16 to X$p$ where $p$ is the number of variables in the $X$ matrix. ## Returns `gPLS` returns an object of class `"gPLS"`, a list that contains the following components: - **X**: the centered and standardized original predictor matrix. - **Y**: the centered and standardized original response vector or matrix. - **ncomp**: the number of components included in the model. - **mode**: the algorithm used to fit the model. - **keepX**: number of $X$ variables kept in the model on each component. - **keepY**: number of $Y$ variables kept in the model on each component. - **mat.c**: matrix of coefficients to be used internally by `predict`. - **variates**: list containing the variates. - **loadings**: list containing the estimated loadings for the $X$ and $Y$ variates. - **names**: list containing the names to be used for individuals and variables. - **tol**: the tolerance used in the iterative algorithm, used for subsequent S3 methods. - **max.iter**: the maximum number of iterations, used for subsequent S3 methods. - **iter**: vector containing the number of iterations for convergence in each component. - **ind.block.x**: a vector of integers describing the grouping of the X variables. - **ind.block.y**: a vector of consecutive integers describing the grouping of the Y variables. ## References Liquet Benoit, Lafaye de Micheaux Pierre , Hejblum Boris, Thiebaut Rodolphe. A group and Sparse Group Partial Least Square approach applied in Genomics context. **Submitted**. Le Cao, K.-A., Martin, P.G.P., Robert-Grani\'e, C. and Besse, P. (2009). Sparse canonical methods for biological data integration: application to a cross-platform study. **BMC Bioinformatics** 10 :34. Le Cao, K.-A., Rossouw, D., Robert-Grani\'e, C. and Besse, P. (2008). A sparse PLS for variable selection when integrating Omics data. **Statistical Applications in Genetics and Molecular Biology** 7 , article 35. Shen, H. and Huang, J. Z. (2008). Sparse principal component analysis via regularized low rank matrix approximation. **Journal of Multivariate Analysis** 99 , 1015-1034. Tenenhaus, M. (1998). **La r\'egression PLS: th\'eorie et pratique**. Paris: Editions Technic. Wold H. (1966). Estimation of principal components and related models by iterative least squares. In: Krishnaiah, P. R. (editors), **Multivariate Analysis**. Academic Press, N.Y., 391-420. ## Author(s) Benoit Liquet and Pierre Lafaye de Micheaux. ## See Also `sPLS`, `sgPLS`, `predict`, `perf`, `cim` and functions from `mixOmics` package: `summary`, `plotIndiv`, `plotVar`, `plot3dIndiv`, `plot3dVar`. ## Examples ```r ## 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.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 ```

gPLS function