SimulGeneExpressionAR1 {G1DBN}R Documentation

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

out<-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,

Author(s)

L`ebre Sophie (http://www3.imperial.ac.uk/theoreticalgenomics/people/slebre),

Chiquet Julien (http://stat.genopole.cnrs.fr/~jchiquet/).

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 <- 20

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

[Package G1DBN version 2.0 Index]