simulateVAR {ebdbNet} | R Documentation |
Function to simulate a simple autoregressive process based on a network adjacency matrix with a given percentage of non-zero elements.
simulateVAR(R, T, P, v, perc)
R |
Number of replicates |
T |
Number of time points |
P |
Number of observations (genes) |
v |
(Px1) vector of gene precisions |
perc |
Percent of non-zero edges in adjacency matrix |
Data are simulated with R replicates, T time points, and P genes, based on a first-order autoregressive process with Gaussian noise. The user can specify the percentage of non-zero edges to be randomly selected in the adjacency matrix.
Dtrue |
Adjacency matrix used to generate data (i.e., the true network) |
y |
Simulated data |
Andrea Rau
library(ebdbNet)
tmp <- runif(1) ## Initialize random number generator
set.seed(125214) ## Save seed
## Simulate data
simData <- simulateVAR(R = 5, T = 10, P = 10, v = rep(10, 10), perc = 0.10)
Dtrue <- simData$Dtrue
y <- simData$y