svds.undirected_factor_model {fastRG} | R Documentation |
Compute the singular value decomposition of the expected adjacency matrix of an undirected factor model
Description
Compute the singular value decomposition of the expected adjacency matrix of an undirected factor model
Usage
## S3 method for class 'undirected_factor_model'
svds(A, k = A$k, 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 satisfyk < ncv \le p
wherep = 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 lengthn
. If a vectorc
is supplied, then SVD is computed on the matrixA - 1c'
, 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 lengthn
. If a vectors
is supplied, then SVD is computed on the matrix(A - 1c')S
, wherec
is the centering vector andS = diag(1/s)
. Ifscale = TRUE
, then the vectors
is computed as the column norm ofA - 1c'
. Default isFALSE
.