Description Usage Arguments Value Author(s) See Also Examples
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.
1 | SimulGeneExpressionAR1(A,B,X0,SigmaEps,n)
|
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. |
A matrix, with n rows (=length) and p columns (=dimension), containing the generated time series,
Lebre Sophie (http://icube-bfo.unistra.fr/en/index.php/Sophie_Lebre),
Chiquet Julien (http://stat.genopole.cnrs.fr/~jchiquet).
SimulNetworkAdjMatrix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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)
}
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