SimulNetworkAdjMatrix: Network object generation

In alishinski/dbn: A package performing Dynamic Bayesian Network inference.

Description

This function builds a object "Network" by simulating a matrix of valued adjacencies from a number of vertices, a proportion of edges and the range of the uniform distribution that is used to build the adjacency matrix. An optional vector of labels may be given.

Usage

SimulNetworkAdjMatrix(Num,EdgesProp,Range,Labels=1:Num)

Arguments

Num	number of genes
EdgesProp	edges proportion in the network
Range	vector with 4 elements specifying range values for the adjacency matrix generation (minimum negative value, maximum negative value, minimum positive value, maximum positive value)
Labels	an optional vector of labels for the edges

Value

a list that contains out$Vertices$Num the number of vertices, out$Vertices$Labels a vector of labels of the vertices, out$Vertices$Regulated a vector of the regulated vertices, out$Edges$Prop the proportion of edges, out$Edges$Num the number of edges, out$AdjMatrix an adjacency matrix (binary) and out$A a valued adjacency matrix.

Author(s)

Lebre Sophie (http://icube-bfo.unistra.fr/en/index.php/Sophie_Lebre),

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

See Also

SimulGeneExpressionAR1, BuildEdges

Examples

library(G1DBN)
## number of genes
p <- 10
## the network - adjacency Matrix
MyNet <- SimulNetworkAdjMatrix(p,0.05,c(-1,0,0,1))
MyNet

## 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$AdjMatrix,B,X0,sigmaEps,n)

alishinski/dbn documentation built on May 12, 2019, 5:36 a.m.