Group Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

Function to perform group Partial Least Squares to classify samples (supervised analysis) and select variables.

Usage

gPLSda(X, Y, ncomp = 2, keepX = rep(ncol(X), ncomp),
       max.iter = 500, tol = 1e-06, ind.block.x)

Arguments

X: numeric matrix of predictors. NAs are allowed.
Y: a factor or a class vector for the discrete outcome.
ncomp: the number of components to include in the model (see Details).
keepX: numeric vector of length ncomp, the number of variables to keep in \(X\)-loadings. By default all variables are kept in the model.
max.iter: integer, the maximum number of iterations.
tol: a positive real, the tolerance used in the iterative algorithm.
ind.block.x: a vector of integers describing the grouping of the \(X\)-variables. (see an example in Details section)

Details

gPLSda function fit gPLS models with \(1, \ldots ,\)ncomp components to the factor or class vector Y. The appropriate indicator (dummy) matrix is created.

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.

Value

sPLSda returns an object of class "sPLSda", a list that contains the following components:

X: the centered and standardized original predictor matrix.
Y: the centered and standardized indicator response vector or matrix.
ind.mat: the indicator matrix.
ncomp: the number of components included in the model.
keepX: number of \(X\) 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: Number of iterations of the algorthm for each component
ind.block.x: a vector of integers describing the grouping of the X variables.

References

Liquet Benoit, Lafaye de Micheaux Pierre , Hejblum Boris, Thiebaut Rodolphe (2016). A group and Sparse Group Partial Least Square approach applied in Genomics context. Bioinformatics.

On sPLS-DA: Le Cao, K.-A., Boitard, S. and Besse, P. (2011). Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 12:253.

Author

Benoit Liquet and Pierre Lafaye de Micheaux.

Examples


data(simuData)
X <- simuData$X
Y <- simuData$Y
ind.block.x <- seq(100, 900, 100)
model <- gPLSda(X, Y, ncomp = 3,ind.block.x=ind.block.x, keepX = c(2, 2, 2))
result.gPLSda <- select.sgpls(model)
result.gPLSda$group.size.X
#>    size comp1 comp2 comp3
#> 1   100     0   100     0
#> 2   100     0     0   100
#> 3   100     0     0     0
#> 4   100   100     0     0
#> 5   100     0     0     0
#> 6   100     0   100     0
#> 7   100     0     0   100
#> 8   100     0     0     0
#> 9   100   100     0     0
#> 10  100     0     0     0

# perf(model,criterion="all",validation="loo") -> res
# res$error.rate