svds.directed_factor_model: Compute the singular value decomposition of the expected adjacency matrix of a directed factor model

Description

Compute the singular value decomposition of the expected adjacency matrix of a directed factor model

# S3 method for directed_factor_model
svds(A, k = min(A$k1, A$k2), nu = k, nv = k, opts = list(), ...)

A: An undirected_factor_model().
k: Desired rank of decomposition.
nu: Number of left singular vectors to be computed. This must be between 0 and k.
nv: Number of right singular vectors to be computed. This must be between 0 and k.
opts: Control parameters related to the computing algorithm. See Details below.
...: Unused, included only for consistency with generic signature.

The opts argument is a list that can supply any of the following parameters:

ncv: Number of Lanzcos basis vectors to use. More vectors will result in faster convergence, but with greater memory use. ncv must be satisfy \(k < ncv \le p\) where p = min(m, n). Default is min(p, max(2*k+1, 20)).
tol: Precision parameter. Default is 1e-10.
maxitr: Maximum number of iterations. Default is 1000.
center: Either a logical value (TRUE/FALSE), or a numeric vector of length \(n\). If a vector \(c\) is supplied, then SVD is computed on the matrix \(A - 1c'\), in an implicit way without actually forming this matrix. center = TRUE has the same effect as center = colMeans(A). Default is FALSE.
scale: Either a logical value (TRUE/FALSE), or a numeric vector of length \(n\). If a vector \(s\) is supplied, then SVD is computed on the matrix \((A - 1c')S\), where \(c\) is the centering vector and \(S = diag(1/s)\). If scale = TRUE, then the vector \(s\) is computed as the column norm of \(A - 1c'\). Default is FALSE.