sim.swn: Simulate Small-world Network

In NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

Description

Simulates a small-world network based on specified topological properties. Data will also be simulated based on the true network structure

Usage

sim.swn(
  nodes,
  n,
  pos = 0.8,
  ran = c(0.3, 0.7),
  nei = 1,
  p = 0.5,
  corr = FALSE,
  replace = NULL,
  ordinal = FALSE,
  ordLevels = NULL
)

Arguments

nodes	Number of nodes in the simulated network
n	Number of cases in the simulated dataset
pos	Proportion of positive correlations in the simulated network
ran	Range of correlations in the simulated network
nei	Adjusts the number of connections each node has to neighboring nodes (see sample_smallworld)
p	Adjusts the rewiring probability (default is .5). p > .5 rewires the simulated network closer to a random network. p < .5 rewires the simulated network closer to a lattice network
corr	Should the simulated network be a correlation network? Defaults to FALSE. Set to TRUE for a simulated correlation network
replace	If noise > 0, then should participants be sampled with replacement? Defaults to TRUE. Set to FALSE to not allow the potential for participants to be consecutively entered into the simulated dataset.
ordinal	Should simulated continuous data be converted to ordinal? Defaults to FALSE. Set to TRUE for simulated ordinal data
ordLevels	If ordinal = TRUE, then how many levels should be used? Defaults to NULL. Set to desired number of intervals (defaults to 5)

Value

Returns a list containing:

simNetwork	Adjacency matrix of the simulated network
simData	Simulated data from sim.correlation in the psych package based on the simulated network
simRho	Simulated correlation from sim.correlation in the psych package

Author(s)

Alexander Christensen <[e-mail]>

References

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 1-9.

Examples

#Continuous data
sim.Norm <- sim.swn(25, 500, nei = 3)

#Ordinal data
sim.Likert <- sim.swn(25, 500, nei = 3, replace = TRUE, ordinal = TRUE, ordLevels = 5)

#Dichotomous data
sim.Binary <- sim.swn(25, 500, nei = 3, replace = TRUE, ordinal = TRUE, ordLevels = 2)

NetworkToolbox documentation built on May 28, 2021, 5:11 p.m.