SimulGeneExpressionAR1 {G1DBN}
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 imes p)
- B
- a column vector (p imes 1)
- X0
- a column vector (p imes 1) containing the values of the process at time 0
- SigmaEps
- a column vector (p imes 1) containing the values of the diagonal of covariance matrix S
- n
- the desired length of the time serie.
Values
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) }
Documentation reproduced from package G1DBN, version 3.1. License: GPL (>= 2)