cca_rrr_cv: Cross-validated Canonical Correlation Analysis via RRR

In ccar3: Canonical Correlation Analysis via Reduced Rank Regression

Cross-validated Canonical Correlation Analysis via RRR

Description

Performs cross-validation to select optimal lambda, fits CCA_rrr. Canonical Correlation Analysis via Reduced Rank Regression (RRR)

Usage

cca_rrr_cv(
  X,
  Y,
  r = 2,
  lambdas = 10^seq(-3, 1.5, length.out = 100),
  kfolds = 14,
  solver = "ADMM",
  parallelize = FALSE,
  LW_Sy = TRUE,
  standardize = TRUE,
  rho = 1,
  thresh_0 = 1e-06,
  niter = 10000,
  thresh = 1e-04,
  verbose = FALSE,
  nb_cores = NULL
)

Arguments

X	Matrix of predictors.
Y	Matrix of responses.
r	Rank of the solution.
lambdas	Sequence of lambda values for cross-validation.
kfolds	Number of folds for cross-validation.
solver	Solver type: "rrr", "CVX", or "ADMM".
parallelize	Logical; should cross-validation be parallelized?
LW_Sy	Whether to use Ledoit-Wolf shrinkage for Sy.
standardize	Logical; should X and Y be scaled.
rho	ADMM parameter.
thresh_0	tolerance for declaring entries non-zero
niter	Maximum number of iterations for ADMM.
thresh	Convergence threshold.
verbose	Logical for verbose output.
nb_cores	Number of cores to use for parallelization (default is all available cores minus 1)

Value

A list with elements:

• U: Canonical direction matrix for X (p x r)

• V: Canonical direction matrix for Y (q x r)

• lambda: Optimal regularisation parameter lambda chosen by CV

• rmse: Mean squared error of prediction (as computed in the CV)

• cor: Canonical correlations

ccar3 documentation built on Sept. 16, 2025, 9:11 a.m.