Usage
sgccak(A, C, c1 = rep(1, length(A)), scheme = "centroid", scale = FALSE,
tol = .Machine$double.eps, init = "svd", bias = TRUE, verbose = TRUE)
Arguments
A
A list that contains the \(J\) blocks of variables from which block components are constructed.
It could be eiher the original matrices (\(X_1, X_2, ..., X_J\)) or the residual matrices (\(X_{h1}, X_{h2}, ..., X_{hJ}\)).
C
A design matrix that describes the relationships between blocks.
c1
A \(1 * J\) vector that contains the value of c1 applied to each block. The L1 bound on a[[j]] is
$$ \|a_{j}\|_{\ell_1} \leq c_1[j] \sqrt{p_j}.$$
with \(p_j\) the number of variables of \(X_j\) and with c1[j] between 0 and 1 (larger L1 bound corresponds to less penalization).
scheme
Either "horst", "factorial" or "centroid" (default: centroid).
scale
If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE).
tol
Stopping value for convergence.
init
Mode of initialization of the SGCCA algorithm. Either by Singular Value Decompostion ("svd") or random ("random") (default: "svd").
bias
Logical value for biaised (\(1/n\)) or unbiaised (\(1/(n-1)\)) estimator of the var/cov.
verbose
Reports progress while computing, if verbose = TRUE (default: TRUE).