multiness_sim {multiness} | R Documentation |
Simulate from the MultiNeSS model
Description
multiness_sim
simulates a realization of the Gaussian
or logistic MultiNeSS model with Gaussian latent positions.
Usage
multiness_sim(n,m,d1,d2,model,sigma,self_loops,opts)
Arguments
n |
A positive integer, the number of nodes. |
m |
A positive integer, the number of layers. |
d1 |
A non-negative integer, the number of common latent dimensions. |
d2 |
A non-negative integer, the number of individual latent dimensions. |
model |
A string which provides choice of model,
either |
sigma |
A positive scalar or numeric vector of length |
self_loops |
A Boolean, if |
opts |
A list, containing additional optional arguments:
|
Details
The common and individual latent positions, and
respectively, are generated as
Gaussian random variables with standard deviation
opts$gamma
, and
dependence controlled by the optional
arguments opts$dependence_type
and opts$rho
.
Under the Gaussian model, the adjacency matrix for layer
has independent Gaussian entries with standard deviation
sigma
and
mean given by
Under the logistic model, the adjacency matrix for layer
has independent Bernoulli entries with mean given by
where denotes the element-wise application of the inverse logistic
link (
expit
) function. Under both models, self_loops
provides
an option to set the diagonal entries of the adjacency matrices to zero.
Value
A list is returned with the realizations of the latent dimensions and the multiplex network:
A |
An array of dimension |
V |
A matrix of dimension |
U |
An array of dimension |
P |
If specified, an array of dimension |
density |
If specified and |
Examples
# gaussian model, uncorrelated latent positions
data1 <- multiness_sim(n=100,m=4,d1=2,d2=2,
model="gaussian")
# logistic model, correlated latent positions
data2 <- multiness_sim(n=100,m=4,d1=2,d2=2,
model="logistic",
self_loops=FALSE,
opts=list(dependence_type="all",rho=.3,return_density=TRUE))