generate_network: Generate graph structure

In bbeomjin/ZILGM: Implementations of local Markov random fields for count data with zero-inflation and overdispersion

Generate graph structure

Description

Generate graph structure.

Usage

generate_network(p, prob, NofHub = 3, type = c("scale-free", "hub", "random"))

Arguments

p	The number of nodes to generate graph structure.
prob	The probability that the edges of the random graph appear.
NofHub	The number of hub nodes in hub graph.
type	Types of graph structure. Either a character string representing one of the graph structures.

Value

A p x p adjacency matrix

References

Choi, H., J. Gim, S. Won, Y. J. Kim, S. Kwon, and C. Park, 2017: Network analysis for count data with excess zeros. BMC genetics, 18, no. 1, 1-10.
Park, B., H. Choi, C. Park, 2021: Negative binomial graphical model with excess zeros.

See Also

zilgm

Examples

require(ZILGM)
set.seed(1)
p = 30; prob = 2 / p;

# Random graph
random_graph = generate_network(p, prob, type = "random")

# Scale-free graph
scale_free_graph = generate_network(p, prob, type = "random")

# Hub graph
hub_graph = generate_network(p, prob, NofHub = 3, type = "hub")

bbeomjin/ZILGM documentation built on Aug. 5, 2023, 5:52 a.m.