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Title:

Canonical Correlation Analysis via Reduced Rank Regression

Version:

0.1.1

Date:

2026-04-10

Author:

Claire Donnat

[aut, cre], Elena Tuzhilina

[aut], Zixuan Wu

[aut]

Maintainer:

Claire Donnat <cdonnat@uchicago.edu>

Description:

Canonical correlation analysis (CCA) via reduced-rank regression with support for regularization and cross-validation. Several methods for estimating CCA in high-dimensional settings are implemented. The first set of methods, cca_rrr() (and variants: cca_group_rrr() and cca_graph_rrr()), assumes that one dataset is high-dimensional and the other is low-dimensional, while the second, ecca() (for Efficient CCA) assumes that both datasets are high-dimensional. For both methods, standard l1 regularization as well as group-lasso regularization are available. cca_graph_rrr further supports total variation regularization when there is a known graph structure among the variables of the high-dimensional dataset. In this case, the loadings of the canonical directions of the high-dimensional dataset are assumed to be smooth on the graph. For more details see Donnat and Tuzhilina (2024) <doi:10.48550/arXiv.2405.19539> and Wu, Tuzhilina and Donnat (2025) <doi:10.48550/arXiv.2507.11160>.

Depends:

R (≥ 3.5.0)

Imports:

methods, magrittr, tidyr, dplyr, foreach, pracma, corpcor, matrixStats, RSpectra

Suggests:

codetools, SMUT, igraph, testthat (≥ 3.0.0), pkgload, rrpack, Matrix, glmnet, CCA, PMA, doParallel, crayon

License:

MIT + file LICENSE

Encoding:

UTF-8

RoxygenNote:

7.3.3

Config/testthat/edition:

NeedsCompilation:

Packaged:

2026-04-10 20:52:29 UTC; clairedonnat

Repository:

CRAN

Date/Publication:

2026-04-29 07:00:02 UTC

ccar3: Canonical Correlation Analysis via Reduced Rank Regression

Description

Canonical correlation analysis (CCA) via reduced-rank regression with support for regularization and cross-validation. Several methods for estimating CCA in high-dimensional settings are implemented. The first set of methods, cca_rrr() (and variants: cca_group_rrr() and cca_graph_rrr()), assumes that one dataset is high-dimensional and the other is low-dimensional, while the second, ecca() (for Efficient CCA) assumes that both datasets are high-dimensional. For both methods, standard l1 regularization as well as group-lasso regularization are available. cca_graph_rrr further supports total variation regularization when there is a known graph structure among the variables of the high-dimensional dataset. In this case, the loadings of the canonical directions of the high-dimensional dataset are assumed to be smooth on the graph. For more details see Donnat and Tuzhilina (2024) doi:10.48550/arXiv.2405.19539 and Wu, Tuzhilina and Donnat (2025) doi:10.48550/arXiv.2507.11160.

Author(s)

Maintainer: Claire Donnat cdonnat@uchicago.edu (ORCID)

Authors:

Elena Tuzhilina elena.tuzhilina@utoronto.ca (ORCID)
Zixuan Wu zixuanwu@uchicago.edu (ORCID)

False Positive Rate (TPR)

Description

This is a function that compares the structure of two matrices A and B. It outputs the number of entries where A is not zero but Bis. A and B need to have the same number of rows and columns

Usage

FPR(A, B, tol = 1e-04)

Arguments

A

A matrix.

B

A matrix (assumed to be the ground truth).

tol

tolerance for declaring the entries non zero.

Value

False Positive Rate (nb of values that are non zero in A and zero in B / (nb of values that are non zero in A))

Examples

A <- matrix(c(1, 0, 0, 1, 1, 0), nrow = 2)
B <- matrix(c(1, 0, 1, 1, 0, 0), nrow = 2)
FPR(A, B)

Function to perform Sparse CCA based on Waaijenborg et al. (2008) REFERENCE Parkhomenko et al. (2009), "Sparse Canonical Correlation Anlaysis with Application to Genomic Data Integration" in Statistical Applications in Genetics and Molecular Biology, Volume 8, Issue 1, Article 1

Description

Function to perform Sparse CCA based on Waaijenborg et al. (2008) REFERENCE Parkhomenko et al. (2009), "Sparse Canonical Correlation Anlaysis with Application to Genomic Data Integration" in Statistical Applications in Genetics and Molecular Biology, Volume 8, Issue 1, Article 1

Usage

SCCA_Parkhomenko(
  x.data,
  y.data,
  n.cv = 5,
  lambda.v.seq = seq(0, 0.2, by = 0.02),
  lambda.u.seq = seq(0, 0.2, by = 0.02),
  Krank = 1,
  standardize = TRUE
)

Arguments

x.data

Matrix of predictors (n x p)

y.data

Matrix of responses (n x q)

n.cv

Number of cross-validation folds (default is 5)

lambda.v.seq

Vector of sparsity parameters for Y (default is a sequence from 0 to 1 with step 0.1)

lambda.u.seq

Vector of sparsity parameters for X (default is a sequence from 0 to 1 with step 0.1)

Krank

Number of canonical components to extract

standardize

Standardize (center and scale) the data matrices X and Y (default is TRUE) before analysis

Value

A list with elements:

U: Canonical direction matrix for X (p x r)
V: Canonical direction matrix for Y (q x r)
cor: Canonical correlations

Function to perform Sparse CCA based on Wilms and Croux (2018) REFERENCE Wilms, I., & Croux, C. (2018). Sparse canonical correlation analysis using alternating regressions. Journal of Computational and Graphical Statistics, 27(1), 1-10.

Description

Function to perform Sparse CCA based on Wilms and Croux (2018) REFERENCE Wilms, I., & Croux, C. (2018). Sparse canonical correlation analysis using alternating regressions. Journal of Computational and Graphical Statistics, 27(1), 1-10.

Usage

SparseCCA(
  X,
  Y,
  lambdaAseq = seq(from = 1, to = 0.01, by = -0.01),
  lambdaBseq = seq(from = 1, to = 0.01, by = -0.01),
  rank,
  selection.criterion = 1,
  n.cv = 5,
  A.initial = NULL,
  B.initial = NULL,
  max.iter = 20,
  conv = 10^-2,
  standardize = TRUE
)

Arguments

X

Matrix of predictors (n x p)

Y

Matrix of responses (n x q)

lambdaAseq

Vector of sparsity parameters for X (default is a sequence from 0 to 1 with step 0.1)

lambdaBseq

Vector of sparsity parameters for Y (default is a sequence from 0 to 1 with step 0.1)

rank

Number of canonical components to extract

selection.criterion

Criterion for selecting the optimal tuning parameter (1 for minimizing difference between test and training sample correlation, 2 for maximizing test sample correlation)

n.cv

Number of cross-validation folds (default is 5)

A.initial

Initial value for the canonical vector A (default is NULL, which uses a canonical ridge solution)

B.initial

Initial value for the canonical vector B (default is NULL, which uses a canonical ridge solution)

max.iter

Maximum number of iterations for convergence (default is 20)

conv

Convergence threshold (default is 1e-2)

standardize

Standardize (center and scale) the data matrices X and Y (default is TRUE) before analysis

Value

A list with elements:

U: Canonical direction matrix for X (p x r)
V: Canonical direction matrix for Y (q x r)
loss: Mean squared error of prediction
cor: Canonical covariances

True Negative Rate (TNR)

Description

This is a function that compares the structure of two matrices A and B. It outputs the number of entries where A and B are both 0. A and B need to have the same number of rows and columns

Usage

TNR(A, B, tol = 1e-04)

Arguments

A

A matrix.

B

A matrix (assumed to be the ground truth)..

tol

tolerance for declaring the entries non zero.

Value

True Negative Rate (nb of values that are zero in A and zero in B / (nb of values that are zero in A))

True Positive Rate (TPR)

Description

This is a function that compares the structure of two matrices A and B. It outputs the number of entries that A and B have in common that are different from zero. A and B need to have the same number of rows and columns

Usage

TPR(A, B, tol = 1e-04)

Arguments

A

A matrix (the estimator).

B

A matrix (assumed to be the ground truth).

tol

tolerance for declaring the entries non zero.

Value

True Positive Rate (nb of values that are non zero in both A and B / (nb of values that are non zero in A))

Examples

A <- matrix(c(1, 0, 0, 1, 1, 0), nrow = 2)
B <- matrix(c(1, 0, 1, 1, 0, 0), nrow = 2)
TPR(A, B)

Sparse CCA by Witten and Tibshirani (2009)

Description

Sparse CCA by Witten and Tibshirani (2009)

Usage

Witten.CV(
  X,
  Y,
  n.cv = 5,
  rank,
  lambdax = matrix(seq(from = 0, to = 1, by = 0.1), nrow = 1),
  lambday = matrix(seq(from = 0, to = 1, by = 0.1), nrow = 1),
  standardize = TRUE
)

Arguments

X

Matrix of predictors (n x p)

Y

Matrix of responses (n x q)

n.cv

Number of cross-validation folds (default is 5)

rank

Number of canonical components to extract

lambdax

Vector of sparsity parameters for X (default is a sequence from 0 to 1 with step 0.1)

lambday

Vector of sparsity parameters for Y (default is a sequence from 0 to 1 with step 0.1)

standardize

Standardize (center and scale) the data matrices X and Y (default is TRUE) before analysis

Value

the appropriate levels of regularisation

Graph-regularized Reduced-Rank Regression for Canonical Correlation Analysis

Description

Solves a sparse canonical correlation problem using a graph-constrained reduced-rank regression formulation. The problem is solved via an ADMM approach.

Usage

cca_graph_rrr(
  X,
  Y,
  Gamma,
  Sx = NULL,
  Sy = NULL,
  Sxy = NULL,
  lambda = 0,
  r,
  standardize = FALSE,
  preprocess = NULL,
  LW_Sy = TRUE,
  rho = 10,
  niter = 10000,
  thresh = 1e-04,
  thresh_0 = 1e-06,
  verbose = FALSE,
  Gamma_dagger = NULL
)

Arguments

X

Matrix of predictors (n x p)

Y

Matrix of responses (n x q)

Gamma

Graph constraint matrix (g x p)

Sx

Optional covariance matrix for X. Kept for backward compatibility; the graph fit now postprocesses directly from X and does not need to form Sx.

Sy

Optional covariance matrix for Y. If NULL, computed similarly; optionally shrunk via Ledoit-Wolf

Sxy

Optional cross-covariance matrix (not currently used)

lambda

Regularization parameter for sparsity

r

Target rank

standardize

Backward-compatible preprocessing flag: TRUE = "scale", FALSE = "center".

preprocess

Preprocessing mode. One of "scale" (center + scale), "center" (center only), or "none".

LW_Sy

Whether to apply Ledoit-Wolf shrinkage to Sy

rho

ADMM penalty parameter

niter

Maximum number of ADMM iterations

thresh

Convergence threshold for ADMM

thresh_0

Threshold for small values in the coefficient matrix (default 1e-6)

verbose

Whether to print diagnostic output

Gamma_dagger

Optional pseudoinverse of Gamma (computed if NULL)

Value

A list with elements:

U: Canonical direction matrix for X (p x r)
V: Canonical direction matrix for Y (q x r)
cor: Canonical covariances
loss: The prediction error 1/n * \| XU - YV\|^2
Lambda: Canonical correlations
B_opt: Estimated reduced-rank coefficient matrix

Graph-regularized Reduced-Rank Regression for Canonical Correlation Analysis with cross validation

Description

Solves a sparse canonical correlation problem using a graph-constrained reduced-rank regression formulation. The problem is solved via an ADMM approach.

Usage

cca_graph_rrr_cv(
  X,
  Y,
  Gamma,
  r = 2,
  lambdas = 10^seq(-3, 1.5, length.out = 10),
  kfolds = 5,
  parallelize = FALSE,
  standardize = TRUE,
  LW_Sy = TRUE,
  preprocess = NULL,
  rho = 10,
  niter = 10000,
  thresh = 1e-04,
  thresh_0 = 1e-06,
  verbose = FALSE,
  Gamma_dagger = NULL,
  nb_cores = NULL
)

Arguments

X

Matrix of predictors (n x p)

Y

Matrix of responses (n x q)

Gamma

Graph constraint matrix (g x p)

r

Target rank

lambdas

Grid of regularization parameters to test for sparsity

kfolds

Number of folds for cross-validation

parallelize

Whether to parallelize cross-validation

standardize

Backward-compatible preprocessing flag: TRUE = "scale", FALSE = "center".

LW_Sy

Whether to apply Ledoit-Wolf shrinkage to Sy

preprocess

Preprocessing mode. One of "scale" (center + scale), "center" (center only), or "none".

rho

ADMM penalty parameter

niter

Maximum number of ADMM iterations

thresh

Convergence threshold for ADMM

thresh_0

Threshold for small values in the coefficient matrix (default 1e-6)

verbose

Whether to print diagnostic output

Gamma_dagger

Optional pseudoinverse of Gamma (computed if NULL)

nb_cores

Number of cores to use for parallelization. Defaults to min(kfolds, 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 covariances
lambda_x: Alias of the selected lambda
lambda_x_se: Foldwise standard error at the selected lambda
lambda_y: Placeholder for symmetry with two-penalty interfaces
lambda_y_se: Placeholder for symmetry with two-penalty interfaces
resultsx: Backward-compatible alias of cv_summary
cv_summary: Data frame with one row per lambda containing mean RMSE and its foldwise standard error
cv_folds: Data frame with fold-level RMSE values for each lambda
Lambda: Canonical correlations from the final fit
B: Estimated reduced-rank coefficient matrix from the final fit
fit: Final fit at the selected lambda

Group-Sparse Canonical Correlation via Reduced-Rank Regression

Description

Performs group-sparse reduced-rank regression for CCA using either ADMM or CVXR solvers.

Usage

cca_group_rrr(
  X,
  Y,
  groups,
  Sx = NULL,
  Sy = NULL,
  Sxy = NULL,
  lambda = 0,
  r,
  standardize = FALSE,
  preprocess = NULL,
  LW_Sy = TRUE,
  solver = "ADMM",
  rho = 1,
  niter = 10000,
  thresh = 1e-04,
  thresh_0 = 1e-06,
  matrix_free_threshold = 4000L,
  cg_tol = 1e-06,
  cg_maxiter = NULL,
  verbose = FALSE
)

Arguments

X

Predictor matrix (n x p)

Y

Response matrix (n x q)

groups

List of index vectors defining groups of predictors

Sx

Optional covariance matrix for X; if NULL computed internally

Sy

Optional covariance matrix for Y; if NULL computed internally

Sxy

Optional cross covariance matrix for X and Y; if NULL computed internally

lambda

Regularization parameter

r

Target rank

standardize

Backward-compatible preprocessing flag: TRUE = "scale", FALSE = "center".

preprocess

Preprocessing mode. One of "scale" (center + scale), "center" (center only), or "none".

LW_Sy

Whether to apply Ledoit-Wolf shrinkage to Sy (default TRUE)

solver

Either "ADMM" or "CVXR". The "CVXR" backend requires the optional package CVXR.

rho

ADMM parameter

niter

Maximum number of ADMM iterations

thresh

Convergence threshold for ADMM

thresh_0

tolerance for declaring entries non-zero

matrix_free_threshold

For ADMM: when both n and p are at least this value, use a matrix-free conjugate-gradient solve instead of forming a dense linear system.

cg_tol

Relative tolerance for the matrix-free conjugate-gradient solve used in ADMM.

cg_maxiter

Maximum iterations for the matrix-free conjugate-gradient solve. Defaults to min(p, 1000).

verbose

Print diagnostics

Value

A list with elements:

U: Canonical direction matrix for X (p x r)
V: Canonical direction matrix for Y (q x r)
cor: Canonical covariances
loss: The prediction error 1/n * \| XU - YV\|^2
Lambda: Canonical correlations
B_opt: Estimated reduced-rank coefficient matrix

Group-Sparse Canonical Correlation via Reduced-Rank Regression with CV

Description

Performs group-sparse reduced-rank regression for CCA using either ADMM or CVXR solvers.

Usage

cca_group_rrr_cv(
  X,
  Y,
  groups,
  r = 2,
  lambdas = 10^seq(-3, 1.5, length.out = 10),
  kfolds = 5,
  parallelize = FALSE,
  standardize = FALSE,
  preprocess = NULL,
  LW_Sy = TRUE,
  solver = "ADMM",
  rho = 1,
  thresh_0 = 0,
  niter = 10000,
  thresh = 1e-04,
  matrix_free_threshold = 4000L,
  cg_tol = 1e-06,
  cg_maxiter = NULL,
  verbose = FALSE,
  nb_cores = NULL
)

Arguments

X

Predictor matrix (n x p)

Y

Response matrix (n x q)

groups

List of index vectors defining groups of predictors

r

Target rank

lambdas

Grid of regularization parameters to try out

kfolds

Nb of folds for the CV procedure

parallelize

Whether to use parallel processing (default is FALSE)

standardize

Backward-compatible preprocessing flag: TRUE = "scale", FALSE = "center".

preprocess

Preprocessing mode. One of "scale" (center + scale), "center" (center only), or "none".

LW_Sy

Whether to apply Ledoit-Wolf shrinkage to Sy (default TRUE)

solver

Either "ADMM" or "CVXR". The "CVXR" backend requires the optional package CVXR.

rho

ADMM parameter

thresh_0

tolerance for declaring entries non-zero

niter

Maximum number of ADMM iterations

thresh

Convergence threshold for ADMM

matrix_free_threshold

For ADMM: when both n and p are at least this value, use a matrix-free conjugate-gradient solve instead of forming a dense linear system.

cg_tol

Relative tolerance for the matrix-free conjugate-gradient solve used in ADMM.

cg_maxiter

Maximum iterations for the matrix-free conjugate-gradient solve. Defaults to min(p, 1000).

verbose

Print diagnostics

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 covariances
lambda_x: Alias of the selected lambda
lambda_x_se: Foldwise standard error at the selected lambda
lambda_y: Placeholder for symmetry with two-penalty interfaces
lambda_y_se: Placeholder for symmetry with two-penalty interfaces
resultsx: Backward-compatible alias of cv_summary
cv_summary: Data frame with one row per lambda containing mean RMSE and its foldwise standard error
cv_folds: Data frame with fold-level RMSE values for each lambda
Lambda: Canonical correlations from the final fit
B: Estimated reduced-rank coefficient matrix from the final fit
fit: Final fit at the selected lambda

Canonical Correlation Analysis via Reduced Rank Regression (RRR)

Description

Estimates canonical directions using various RRR solvers and penalties.

Usage

cca_rrr(
  X,
  Y,
  Sx = NULL,
  Sy = NULL,
  lambda = 0,
  r,
  highdim = TRUE,
  solver = "ADMM",
  LW_Sy = TRUE,
  mode = "sqrtm_norm",
  standardize = FALSE,
  preprocess = NULL,
  rho = 1,
  niter = 10000,
  thresh = 1e-04,
  thresh_0 = 0,
  matrix_free_threshold = 4000L,
  cg_tol = 1e-06,
  cg_maxiter = NULL,
  verbose = FALSE
)

Arguments

X

Matrix of predictors.

Y

Matrix of responses.

Sx

Optional X covariance matrix.

Sy

Optional Y covariance matrix.

lambda

Regularization parameter.

r

Rank of the solution.

highdim

Boolean for high-dimensional regime.

solver

Solver type: "rrr", "CVX", or "ADMM". The "CVX" backend requires the optional package CVXR.

LW_Sy

Whether to use Ledoit-Wolf shrinkage for Sy.

mode

Mode for postprocessing the RRR solution. One of "sqrtm_norm" (default) or "product_norm". Legacy aliases "new" and "old" are also accepted. The former whitens the canonical variates to have identity covariance, while the latter does not whiten and instead returns the raw SVD factors of the RRR solution. The "product_norm" mode may be more interpretable in some cases but can yield canonical variates with very different scales and is not guaranteed to be numerically stable when the RRR solution is very low-rank or nearly low-rank.

standardize

Backward-compatible preprocessing flag: TRUE = "scale", FALSE = "center".

preprocess

rho

ADMM parameter.

niter

Maximum number of iterations for ADMM.

thresh

Convergence threshold.

thresh_0

For the ADMM solver: Set entries whose absolute value is below this to 0 (default 1e-6).

matrix_free_threshold

For ADMM: when both n and p are at least this value, use a matrix-free conjugate-gradient solve instead of forming a dense linear system.

cg_tol

Relative tolerance for the matrix-free conjugate-gradient solve used in ADMM.

cg_maxiter

Maximum iterations for the matrix-free conjugate-gradient solve. Defaults to min(p, 1000).

verbose

Logical for verbose output.

Value

A list with elements:

U: Canonical direction matrix for X (p x r)
V: Canonical direction matrix for Y (q x r)
cor: Canonical covariances
loss: The prediction error 1/n * || XU - YV ||^2

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 = 10,
  solver = "ADMM",
  mode = "sqrtm_norm",
  parallelize = FALSE,
  LW_Sy = TRUE,
  standardize = FALSE,
  preprocess = NULL,
  cv_metric = "mse",
  rho = 1,
  thresh_0 = 0,
  niter = 10000,
  matrix_free_threshold = 4000L,
  cg_tol = 1e-06,
  cg_maxiter = NULL,
  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". The "CVX" backend requires the optional package CVXR.

mode

parallelize

Logical; should cross-validation be parallelized?

LW_Sy

Whether to use Ledoit-Wolf shrinkage for Sy.

standardize

Backward-compatible preprocessing flag: TRUE = "scale", FALSE = "center".

preprocess

Preprocessing mode. One of "scale" (center + scale), "center" (center only), or "none". Logical values are still accepted for backward compatibility: TRUE uses standardize, FALSE skips preprocessing. Data are preprocessed once up front, and fold fits reuse those transformed matrices without re-centering or re-scaling inside each fold.

cv_metric

Cross-validation metric. Use "mse" to minimize held-out prediction error or "correlation" to maximize held-out association between X %*% U and Y %*% V.

rho

ADMM parameter.

thresh_0

tolerance for declaring entries non-zero

niter

Maximum number of iterations for ADMM.

matrix_free_threshold

For ADMM: when both n and p are at least this value, use a matrix-free conjugate-gradient solve instead of forming a dense linear system.

cg_tol

Relative tolerance for the matrix-free conjugate-gradient solve used in ADMM.

cg_maxiter

Maximum iterations for the matrix-free conjugate-gradient solve. Defaults to min(p, 1000).

thresh

Convergence threshold.

verbose

Logical for verbose output.

nb_cores

Number of cores to use for parallelization. Defaults to min(kfolds, 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: Backward-compatible optimization objective. For cv_metric = "mse" this is the held-out mean squared error; for cv_metric = "correlation" it is -cv_score so that smaller still means better.
cv_score: Raw held-out cross-validation score averaged across folds.
cv_metric: The metric used to score lambdas during cross-validation.
cor: Canonical correlations at the selected lambda
lambda_x: Alias of the selected lambda
lambda_x_se: Foldwise standard error at the selected lambda
lambda_y: Placeholder for symmetry with two-penalty interfaces
lambda_y_se: Placeholder for symmetry with two-penalty interfaces
resultsx: Backward-compatible alias of cv_summary
cv_summary: Data frame with one row per lambda containing the mean CV score and its foldwise standard error.
cv_folds: Data frame with fold-level CV scores for each lambda.
Lambda: Canonical correlations from the final fit
B: Estimated reduced-rank coefficient matrix from the final fit
fit: Final fit at the selected lambda

Efficient CCA for Two High-Dimensional Views

Description

Fits sparse canonical directions with an ADMM-based reduced-rank regression formulation tailored to the setting where both views are high-dimensional.

Usage

ecca(
  X,
  Y,
  lambda = 0,
  groups = NULL,
  r = 2,
  standardize = FALSE,
  rho = 1,
  B0 = NULL,
  eps = 1e-04,
  maxiter = 500,
  verbose = TRUE,
  epsilon_sv = 1e-08,
  ridge_whiten = 1e-08
)

Arguments

X

Predictor matrix (n x p).

Y

Response matrix (n x q).

lambda

Regularization parameter.

groups

Optional group structure for blockwise sparsity.

r

Target rank.

standardize

Whether to scale variables after centering.

rho

ADMM penalty parameter.

B0

Optional warm start for the coefficient matrix.

eps

Convergence tolerance for ADMM.

maxiter

Maximum number of ADMM iterations.

verbose

Whether to print diagnostics.

epsilon_sv

Numerical threshold used to discard near-zero singular values.

ridge_whiten

Ridge added when whitening Gram matrices.

Value

A list containing the estimated canonical directions, canonical correlations, the fitted coefficient matrix, preprocessing metadata, and convergence information.

Cross-Validated Efficient CCA

Description

Selects a regularization parameter for ecca() by cross-validation and refits the final model at the selected value.

Usage

ecca.cv(
  X,
  Y,
  lambdas = 0,
  groups = NULL,
  r = 2,
  standardize = FALSE,
  rho = 1,
  B0 = NULL,
  nfold = 5,
  select = "lambda.min",
  eps = 0.001,
  maxiter = 1000,
  verbose = FALSE,
  maxiter_cv = 300,
  parallel = FALSE,
  nb_cores = NULL,
  set_seed_cv = NULL,
  scoring_method = c("mse", "trace"),
  cv_use_median = FALSE,
  dense = TRUE,
  optimized = FALSE,
  epsilon_sv = 1e-08,
  ridge_whiten = 1e-08
)

Arguments

X

Predictor matrix (n x p).

Y

Response matrix (n x q).

lambdas

Candidate regularization values.

groups

Optional group structure for blockwise sparsity.

r

Target rank.

standardize

Whether to scale variables after centering.

rho

ADMM penalty parameter.

B0

Optional warm start for the coefficient matrix.

nfold

Number of cross-validation folds.

select

Selection rule for the final lambda. One of "lambda.min" or "lambda.1se".

eps

Convergence tolerance for the final ADMM refit.

maxiter

Maximum iterations for the final ADMM refit.

verbose

Whether to print diagnostics.

maxiter_cv

Maximum iterations used inside the cross-validation fits.

parallel

Whether to parallelize cross-validation.

nb_cores

Number of worker processes to use when parallel = TRUE.

set_seed_cv

Optional random seed for fold generation.

scoring_method

Cross-validation score to optimize. One of "mse" or "trace".

cv_use_median

Whether to aggregate fold scores with the median instead of the mean.

dense

Retained for backward compatibility.

optimized

Retained for backward compatibility.

epsilon_sv

Numerical threshold used to discard near-zero singular values.

ridge_whiten

Ridge added when whitening Gram matrices.

Value

A list with the final fit, selected lambda, and cross-validation scores when more than one lambda is supplied.

Return the edge incidence matrix of an igraph graph

Description

Return the edge incidence matrix of an igraph graph

Usage

get_edge_incidence(g, weight = 1)

Arguments

g

igraph graph object.

weight

edge weights.

Value

Edge incidence matrix of the graph g, with +weight for the source node and -weight for the target node.

Metrics for subspaces

Description

Calculate principal angles between subspace spanned by the columns of a and the subspace spanned by the columns of b

Usage

principal_angles(a, b)

Arguments

a

A matrix whose columns span a subspace.

b

A matrix whose columns span a subspace.

Value

a vector of principal angles (in radians)

Examples

a <- matrix(rnorm(9), 3, 3)
b <- matrix(rnorm(9), 3, 3)
principal_angles(a, b)

Function to perform regular (low dimensional) canonical correlation analysis (CCA

Description

Function to perform regular (low dimensional) canonical correlation analysis (CCA

Usage

regular_cca(X, Y, rank)

Arguments

X

Matrix of predictors (n x p)

Y

Matrix of responses (n x q)

rank

Number of canonical components to extract

Value

A list with elements:

U: Canonical direction matrix for X (p x r)
V: Canonical direction matrix for Y (q x r)
cor: Canonical covariances

SinTheta distance between subspaces

Description

Calculate the distance spanned by the columns of A and the subspace spanned by the columns of B, defined as ||UU^T - VV^T||_F / sqrt(2)

Usage

sinTheta(U, V)

Arguments

U

A matrix whose columns span a subspace.

V

A matrix whose columns span a subspace.

Value

sinTheta distance between the two subspaces spanned by the matrices A and B, defined as ||UU^T - VV^T||_F / sqrt(2)

Additional Benchmarks for Sparse CCA Methods

Description

Additional Benchmarks for Sparse CCA Methods

Usage

sparse_CCA_benchmarks(
  X_train,
  Y_train,
  S = NULL,
  rank = 2,
  kfolds = 5,
  method.type = "FIT_SAR_CV",
  lambdax = 10^seq(from = -3, to = 2, length = 10),
  lambday = c(0, 1e-07, 1e-06, 1e-05),
  standardize = TRUE
)

Arguments

X_train

Matrix of predictors (n x p)

Y_train

Matrix of responses (n x q)

S

Optional covariance matrix (default is NULL, which computes it from X_train and Y_train)

rank

Target rank for the CCA (default is 2)

kfolds

Number of cross-validation folds (default is 5)

method.type

Type of method to use for Sparse CCA (default is "FIT_SAR_CV"). Choices include "FIT_SAR_BIC", "FIT_SAR_CV", "Witten_Perm", "Witten.CV", and "SCCA_Parkhomenko".

lambdax

Vector of sparsity parameters for X (default is a sequence from 0 to 1 with step 0.1)

lambday

Vector of sparsity parameters for Y (default is a sequence from 0 to 1 with step 0.1)

standardize

Standardize (center and scale) the data matrices X and Y (default is TRUE) before analysis

Value

A matrix (p+q)x r containing the canonical directions for X and Y.

Subdistance between subspaces

Description

Calculate subdistance between subspace spanned by the columns of a and the subspace spanned by the columns of b

Usage

subdistance(A, B)

Arguments

A

A matrix whose columns span a subspace.

B

A matrix whose columns span a subspace.

Value

subdistance between the two subspaces spanned by the matrices A and B, defined as min(O orthogonal) ||AO-B||_F