signalnetwork: Sample a random signalling network.

In ddepn: Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data.

Description

For n nodes, sample a network with nstim stimuli and cstim combinatorial stimuli.

Usage

signalnetwork(n=10, nstim=2, cstim=0, prop.inh=.2,plot=F,gamma=1,B=NULL,
	V=NULL,stimuli=NULL)

Arguments

n	Integer. Number of nodes.
nstim	Integer. Number of stimuli.
cstim	Integer. Number of combinatorial stimuli.
prop.inh	Proportion (in [0;1]) of the number of activating edges to be included as inhibiting edges in the network.
plot	Boolean. If TRUE, a plot of the generated graph is drawn.
gamma	Double. Strength of power law decay. Used for simulating the number of outgoing edges.
B	The prior edge probability matrix.
V	Vector of strings. Names of the nodes.
stimuli	List. See ddepn for an explanation.

Details

Simulates an artificial signalling network. Starts at nstim random stimuli and selects random children, to which activation edges are drawn. These children are the new stimuli and the procedure is repeated until all nodes were reached by activating edges. Finally, prop.inh*numedges inhibiting edges are added randomly. The number of stimuli combinations cstim is limited by sum_{k=2}^n {k \choose n}. If defined, B gives a matrix containing prior probabilities for each possible edge in the network.

Value

List containing the adjacency list phi and the list of all stimuli.

Author(s)

Christian Bender

See Also

simulatedata

Examples

## Not run: 
library(ddepn)
signalnetwork(n=10, nstim=4, cstim=4, prop.inh=.4, plot=TRUE)

## End(Not run)

ddepn documentation built on May 2, 2019, 4:42 p.m.