simulatedata: simulatedata

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

Description

Generate artificial timecourse data for network inference.

Usage

simulatedata(phi, mu.bg=0, sd.bg=0.1,
		mu.signal.a=1, sd.signal.a=0.5,
		mu.signal.i=-1, sd.signal.i=0.5,
		stimulus=sample(nrow(phi),2),TT=10,R.t=4,R.b=3,
		plot=FALSE,stimuli=NULL, allow.stim.off=TRUE) 

Arguments

phi	The network for which data should be simulated.
mu.bg	mean for passive state.
sd.bg	sd for passive state.
mu.signal.a	mean for active state of type activation.
sd.signal.a	sd for active state of type activation.
mu.signal.i	mean for active state of type inhibition.
sd.signal.i	sd for active state of type inhibition.
stimulus	Where the network gets stimulated. Are set to 1 for the effect propagation.
TT	Number of timepoints.
R.t	Number of technical replicates.
R.b	Number of biological replicates.
plot	Should a plot be generated after data generation.
stimuli	List of input stimuli.
allow.stim.off	Boolean. Allow the stimuli to become inactive at some point. See also ddepn.

Details

TODO

Value

Artificial datasets for a given network.

Author(s)

Christian Bender

Examples

## Not run: 
library(ddepn)
n <- 8
phi <- matrix(sample(c(0,1,2),n*n,replace=TRUE),nrow=n,dimnames=list(LETTERS[1:n],LETTERS[1:n]))
simulatedata(phi, mu.bg=0, sd.bg=0.1,
		mu.signal.a=1, sd.signal.a=0.5,
		mu.signal.i=-1, sd.signal.i=0.5,
		stimulus=sample(nrow(phi),2),TT=10,R.t=4,R.b=3,
		plot=TRUE)

## End(Not run)

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