genplsc: Canonical Generalized PLS (alias)

In genpca: Generalized Principal Component Analysis

Canonical Generalized PLS (alias)

Description

Convenience alias for genpls(); computes canonical generalized PLS (PLS-SVD/GPLSSVD). See ?genpls for full documentation.

Usage

genplsc(
  X,
  Y,
  Ax = NULL,
  Ay = NULL,
  Mx = NULL,
  My = NULL,
  ncomp = 2,
  preproc_x = multivarious::pass(),
  preproc_y = multivarious::pass(),
  svd_backend = c("eigencore", "irlba", "RSpectra"),
  svd_opts = list(tol = 1e-07, maxitr = 1000),
  constraints_remedy = c("error", "ridge", "clip", "identity"),
  verbose = FALSE
)

Arguments

X	Numeric or Matrix, n x p.
Y	Numeric or Matrix, n x q. Must have same n as X.
Ax	Column metric for X (W_X): vector/diagonal/matrix; NULL means identity.
Ay	Column metric for Y (W_Y): vector/diagonal/matrix; NULL means identity.
Mx	Row metric for X (M_X): vector/diagonal/matrix; NULL means identity.
My	Row metric for Y (M_Y): vector/diagonal/matrix; NULL means identity.
ncomp	Number of components to extract (rank-k). Default 2.
preproc_x, preproc_y	Optional multivarious preprocessors (e.g., center()). Defaults to multivarious::pass() (no-op).
svd_backend	Character, one of "eigencore" (default) or "irlba" for the iterative SVD. This choice only matters for larger problems: whenever both X and Y have at most 64 columns after preprocessing, the operator materializes S densely and computes a direct svd(), ignoring svd_backend entirely (see gplssvd_op()).
svd_opts	List of options: tol for both backends and maxitr for irlba only. An incomplete eigencore solve raises an error of class genpca_solver_nonconvergence; no unchecked fit is returned.
constraints_remedy	What to do with a metric that is not positive semi-definite: "error" (default), "ridge", "clip" or "identity"; repairs emit a genpca_metric_repaired warning. See genpca().
verbose	Logical; print brief progress messages.

Value

An object of class c("genpls", "cross_projector", "projector") with the same structure as genpls() returns (X-/Y-weights vx/vy, singular values d, generalized weights p/q, scores fi/fj, latent variables lx/ly, ncomp, and backend); see ?genpls for the definition of each slot.

References

Beaton, D. (2020). Generalized eigen, singular value, and partial least squares decompositions: The GSVD package. (Eqs. 10-14). arXiv:2010.14734.

See Also

genpls()

Examples

set.seed(1)
X <- matrix(rnorm(60 * 5), 60, 5)
Y <- matrix(rnorm(60 * 4), 60, 4)
fit <- genplsc(X, Y, ncomp = 2,
               preproc_x = multivarious::center(),
               preproc_y = multivarious::center())
fit$d

genpca documentation built on Sept. 17, 2026, 1:09 a.m.