G1DBN (version 3.1.1)

SimulGeneExpressionAR1: First order multivariate Auto-Regressive time series generation

Description

This function generates multivariate time series according to the following first order Auto-Regressive process, $$X(t)= A X(t-1) + B + \varepsilon(t),$$ where $\varepsilon(t)$ follows a zero-centered multivariate gaussian distribution whose variance matrix $S$ is diagonal.

Usage

SimulGeneExpressionAR1(A,B,X0,SigmaEps,n)

Arguments

A

a matrix ($p \times p$)

B

a column vector ($p \times 1$)

X0

a column vector ($p \times 1$) containing the values of the process at time 0

SigmaEps

a column vector ($p \times 1$) containing the values of the diagonal of covariance matrix S

n

the desired length of the time serie.

Value

• A matrix, with $n$ rows (=length) and $p$ columns (=dimension), containing the generated time series,

See Also

SimulNetworkAdjMatrix

Examples

library(G1DBN)
## number of genes
p <- 20
## the network - adjacency Matrix
MyNet <- SimulNetworkAdjMatrix(p,0.05,c(-1,0,0,1))

## initializing the B vector
B <- runif(p,0,0.5)
## initializing the variance of the noise
sigmaEps <- runif(p,0.1,0.8)
## initializing the process Xt
X0 <- B + rnorm(p,0,sigmaEps*10)
## number of time points
n <- 30

## the AR(1) time series process
Xn <- SimulGeneExpressionAR1(MyNet$A,B,X0,sigmaEps,n)

plot(1:n, Xn[,1],type="l", xlab="Time t", ylab="X(t)",
main="Simulated AR(1) time series", ylim=c(min(Xn),max(Xn)))

for (i in 2:p){
  lines(1:n,Xn[,i],col=i)
}