# createA: Generation of the VAR(1) autoregression coefficient matrix. In wvanwie/ragt2ridges: Ridge Estimation of Vector Auto-Regressive (VAR) Processes

## Description

Generates autoregression coefficient matrices of the VAR(1) with various type of topologies

## Usage

 1 2 createA(p, topology, nonzeroA=0, nCliques=1, nHubs=1, nBands=1, percZeros=0.9, stationary=TRUE) 

## Arguments

 p A positive integer specifying the dimension of the square matrix \mathbf{A}. topology Topology to impose on \mathbf{A}: a character equalling either clique, hub, chain, or random. nonzeroA Numeric, value that nonzero elements of \mathbf{A} will assume. If equal to zero, a random value from the interval [-1,1] is sampled. nCliques When topology="clique", this positive integer specifies number of cliques. nHubs When topology="hub", this positive integer specifies number of hubs. nBands When topology="chain", this positive integer specifies number of bands. percZeros When topology="random", the probability with which zero elements of \mathbf{A} are to be sampled. stationary A logical: should the generated \mathbf{A} be stationary?

## Value

A matrix with autoregression coefficient matrix \mathbf{A} of the VAR(1) model.

## Author(s)

Viktorian Miok, Wessel N. van Wieringen <[email protected]>.

## References

Miok, V., Wilting, S.M., Van Wieringen, W.N. (2016), "Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data", Biometrical Journal, 59(1), 172-191.

createS, dataVAR1.
 1 2 3 4 5 6 # create the VAR(1) parameters A <- createA(10, topology="clique", nonzeroA=0.1, nClique=4) Se <- createS(1000, 10, "star") # sample data from the VAR(1) model with above parameters Y <- dataVAR1(4, 8, A, Se)