simulate_GRN: Generate gene regulatory network (GRN) data from structural...
In igbucur/BFCS: Bayes Factors for Covariance Structures

Description Usage Arguments Value Examples

Generate gene regulatory network (GRN) data from structural equation model.

simulate_GRN(
  ngen,
  nexp,
  nobs,
  prob_edge,
  seed = NULL,
  lwr_edge_weight = -1,
  upr_edge_weight = 1
)

`ngen`	Integer number of genetic markers.
`nexp`	Integer number of expression traits.
`nobs`	Integer number of observations in the generated data set.
`prob_edge`	Numeric probability of having a causal interaction (edge) between two expression traits. Passed on to randomDAG.
`seed`	Integer seed for reproducible random number generation.
`lwr_edge_weight`	Lower limit of edge weight (causal strength T_i -> T_j)
`upr_edge_weight`	Upper limit of edge weight (causal strength T_i -> T_j)

A data set consisting of nobs samples from a random generated gene regulatory network. This includes ngen binomially distributed variables representing the genetic markers associated with the genetic makeup of each individual observation. It also includes nexp columns representing the gene expression levels of nexp traits, which have an underlying causal graph describing their interactions.