generate.data.targetmodel: Target model based on predictor trajectories
In CausalKinetiX: Learning Stable Structures in Kinetic Systems

Description Usage Arguments Details Value Author(s) References See Also Examples

Generate sample data from the target model based on predictor trajectories.

1 2	generate.data.targetmodel(env = rep(1, 10), noise.sd = 0.01, L = 15, d = 7, seed = NA)

`env`	integer vector of length n encoding to which experiment each repetition belongs.
`noise.sd`	numerical value specifying the standard deviation of the noise.
`L`	number of time points for evaluation.
`d`	number of total variables (d-1 preditor variables).
`seed`	random seed. Does not work if a "Detected blow-up" warning shows up.

For further details see the references.

list consisting of the following elements

`simulated.data`	D-matrix of noisy data.
`time`	vector containing time points
`env`	vector specifying the experimental environment.
`true.model`	vector specifying the target equation model.
`target`	target variable.

Niklas Pfister, Stefan Bauer and Jonas Peters

Pfister, N., S. Bauer, J. Peters (2018). Identifying Causal Structure in Large-Scale Kinetic Systems ArXiv e-prints (arXiv:1810.11776).

The functions generate.data.maillard and generate.data.hidden allow to simulate ODE data from two additional models.

simulation.obj <- generate.data.targetmodel(env=rep(1:5, 3),
                                            L=15,
                                            d=5)

D <- simulation.obj$simulated.data
fulldata <- simulation.obj$simulated.model
time <- simulation.obj$time
plot(time, D[1,1:length(time)], col="red", pch=19)
legend("topright", c("observations"),
       col=c("red"), pch=c(19))