View source: R/sugm.generator.R
sugm.generator | R Documentation |
Implements the data generation from multivariate normal distributions with different graph structures, including "random"
, "hub"
, "cluster"
, "band"
, and "scale-free"
.
sugm.generator(n = 200, d = 50, graph = "random", v = NULL, u = NULL, g = NULL, prob = NULL, seed = NULL, vis = FALSE, verbose = TRUE)
n |
The number of observations (sample size). The default value is 200. |
d |
The number of variables (dimension). For |
graph |
The graph structure with 5 options: |
v |
The off-diagonal elements of the precision matrix, controlling the magnitude of partial correlations with |
u |
A positive number being added to the diagonal elements of the precision matrix, to control the magnitude of partial correlations. The default value is 0.1. |
g |
For |
prob |
For |
seed |
Set seed for data generation. The default value is 1. |
vis |
Visualize the adjacency matrix of the true graph structure, the graph pattern, the covariance matrix and the empirical covariance matrix. The default value is |
verbose |
If |
Given the adjacency matrix theta
, the graph patterns are generated as below:
(I) "random"
: Each pair of off-diagonal elements are randomly set theta[i,j]=theta[j,i]=1
for i!=j
with probability prob
, and 0
other wise. It results in about d*(d-1)*prob/2
edges in the graph.
(II)"hub"
:The row/columns are evenly partitioned into g
disjoint groups. Each group is associated with a "center" row i
in that group. Each pair of off-diagonal elements are set theta[i,j]=theta[j,i]=1
for i!=j
if j
also belongs to the same group as i
and 0
otherwise. It results in d - g
edges in the graph.
(III)"cluster"
:The row/columns are evenly partitioned into g
disjoint groups. Each pair of off-diagonal elements are set theta[i,j]=theta[j,i]=1
for i!=j
with the probability prob
if both i
and j
belong to the same group, and 0
other wise. It results in about g*(d/g)*(d/g-1)*prob/2
edges in the graph.
(IV)"band"
: The off-diagonal elements are set to be theta[i,j]=1
if 1<=|i-j|<=g
and 0
other wise. It results in (2d-1-g)*g/2
edges in the graph.
(V) "scale-free"
: The graph is generated using B-A algorithm. The initial graph has two connected nodes and each new node is connected to only one node in the existing graph with the probability proportional to the degree of the each node in the existing graph. It results in d
edges in the graph.
The adjacency matrix theta
has all diagonal elements equal to 0
. To obtain a positive definite covariance matrix, the smallest eigenvalue of theta*v
(denoted by e
) is computed. Then we set the covariance matrix equal to cov2cor(solve(theta*v+(|e|+0.1+u)*I))
to generate multivariate normal data.
An object with S3 class "sim" is returned:
data |
The |
sigma |
The covariance matrix for the generated data |
omega |
The precision matrix for the generated data |
sigmahat |
The empirical covariance matrix for the generated data |
theta |
The adjacency matrix of true graph structure (in sparse matrix representation) for the generated data |
Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu
Maintainer: Xingguo Li <xingguo.leo@gmail.com>
flare
and flare-package
## load package required library(flare) ## band graph with bandwidth 3 L = sugm.generator(graph = "band", g = 3) plot(L) ## random sparse graph L = sugm.generator(vis = TRUE) ## hub graph with 6 hubs L = sugm.generator(graph = "hub", g = 6, vis = TRUE) ## cluster graph with 8 clusters L = sugm.generator(graph = "cluster", g = 8, vis = TRUE) ## scale-free graphs L = sugm.generator(graph="scale-free", vis = TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.