Simulate data following a Exploratory Graph Model (EGM)

Function to simulate data based on EGM

Usage

simEGM(
  communities,
  variables,
  loadings,
  cross.loadings = 0.01,
  correlations,
  sample.size,
  p.in = 0.95,
  p.out = 0.8,
  max.iterations = 1000
)

Arguments

communities

Numeric (length = 1). Number of communities to generate

variables

Numeric vector (length = 1 or communities). Number of variables per community

loadings

Numeric (length = 1). Magnitude of the assigned network loadings. Uses the same magnitude as factors loadings

Uses runif(n, min = value - 0.025, max = value + 0.025) for some jitter in the loadings

cross.loadings

Numeric (length = 1). Standard deviation of a normal distribution with a mean of zero (n, mean = 0, sd = value). Defaults to 0.01

correlations

Numeric (length = 1). Magnitude of the community correlations

Uses runif(n, min = value - 0.015, max = value + 0.015) for some jitter in the correlations

sample.size

Numeric (length = 1). Number of observations to generate

p.in

Numeric (length = 1). Sets the probability of retaining an edge within communities. Single values are applied to all communities. Defaults to 0.95

p.out

Numeric (length = 1 or communities). Sets the probability of retaining an edge between communities. Single values are applied to all communities. Defaults to 0.80

max.iterations

Numeric (length = 1). Number of iterations to attempt to get convergence before erroring out. Defaults to 1000

Author

Hudson F. Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>

Examples

simulated <- simEGM(
  communities = 2, variables = 6,
  loadings = 0.55, # use standard factor loading sizes
  correlations = 0.30,
  sample.size = 1000
)