CausalKinetiX (version 0.2.1)

generate.data.targetmodel: Target model based on predictor trajectories

Description

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

Usage

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

Arguments

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.

Value

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.

Details

For further details see the references.

References

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

See Also

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

Examples

# NOT RUN {
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))
# }