SCCA_Parkhomenko: Function to perform Sparse CCA based on Waaijenborg et al....

In ccar3: Canonical Correlation Analysis via Reduced Rank Regression

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

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