SimulGeneExpressionAR1 {G1DBN} | R Documentation |
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.
out<-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,
L`ebre Sophie (http://www3.imperial.ac.uk/theoreticalgenomics/people/slebre),
Chiquet Julien (http://stat.genopole.cnrs.fr/~jchiquet/).
SimulNetworkAdjMatrix
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 <- 20 ## the AR(1) times series process Xn <- SimulGeneExpressionAR1(MyNet$A,B,X0,sigmaEps,n)