svds.directed_factor_model {fastRG} | R Documentation |
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 |
|
k |
Desired rank of decomposition. |
nu |
Number of left singular vectors to be computed. This must
be between 0 and |
nv |
Number of right singular vectors to be computed. This must
be between 0 and |
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 satisfywhere
p = min(m, n)
. Default ismin(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. If a vector
is supplied, then SVD is computed on the matrix
, in an implicit way without actually forming this matrix.
center = TRUE
has the same effect ascenter = colMeans(A)
. Default isFALSE
.scale
Either a logical value (
TRUE
/FALSE
), or a numeric vector of length. If a vector
is supplied, then SVD is computed on the matrix
, where
is the centering vector and
. If
scale = TRUE
, then the vectoris computed as the column norm of
. Default is
FALSE
.