View source: R/expected-spectra.R
svds.directed_factor_model | R Documentation |
Compute the singular value decomposition of the expected adjacency matrix of a directed factor model
## S3 method for class 'directed_factor_model'
svds(A, k = min(A$k1, A$k2), nu = k, nv = k, opts = list(), ...)
A |
An |
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. |
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
.
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