gaussian_snapshot_bs {fase} | R Documentation |
Simulate Gaussian edge networks with B-spline latent processes
Description
gaussian_snapshot_bs
simulates a realization of a functional network
with Gaussian edges, according to an inner product latent process model.
The latent processes are generated from a -spline basis with equally
spaced knots.
Usage
gaussian_snapshot_bs(n,d,m,self_loops=TRUE,
spline_design,sigma_edge=1,
process_options)
Arguments
n |
A positive integer, the number of nodes. |
d |
A positive integer, the number of latent space dimensions. |
m |
A positive integer, the number of snapshots.
If this argument is not specified, it
is determined from the snapshot index vector |
self_loops |
A Boolean, if |
spline_design |
A list, describing the
|
sigma_edge |
A positive scalar,
the entry-wise standard deviation for the Gaussian edge variables.
Defaults to |
process_options |
A list, containing additional optional arguments:
|
Details
The spline design of the functional network data (snapshot indices,
basis dimension) is generated using the information provided in
spline_design
, producing a -dimensional cubic
-spline basis with equally spaced knots.
The latent process basis coordinates are generated as iid
Gaussian random variables with standard deviation
process_options$sigma_coord
. Each latent process is given by
Then, the symmetric adjacency matrix for
snapshot
has independent Gaussian entries
with standard deviation
sigma_edge
and mean
for (or
with no self loops).
Value
A list is returned with the realizations of the basis coordinates, spline design, and the multiplex network snapshots:
A |
An array of dimension |
W |
An array of dimension |
spline_design |
A list, describing the
|
Examples
# Gaussian edge data with B-spline latent processes, Gaussian coordinates
# NOTE: x_vec is automatically populated given m
data <- gaussian_snapshot_bs(n=100,d=4,m=100,
self_loops=FALSE,
spline_design=list(q=12),
sigma_edge=3,
process_options=list(sigma_coord=.75))