svds.directed_factor_model {fastRG}

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

Usage

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

Arguments

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.

Details

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.