Description Usage Arguments Details Value Author(s) References See Also Examples
Generate sample data from the hidden variable model.
1 2 3 4 5 |
env |
integer vector of length n encoding to which experiment each repetition belongs. |
L |
number of time points for evaluation. |
par.noise |
list of parameters that specify the added
noise. |
intervention |
string specifying type of intervention. Currently three type of interventions are implemented "initial" (only intervene on intial values), "blockreactions" (intervene by blocking random reactions) or "intial_blockreactions" (intervene on both initial values and blockreactions"). |
intervention.par |
if intervention is either "blockreactions"
or "initial_blockreactions", the rate constant k_7 is set to a
noisy version of k_4. This parameter specifies size of this
noise, more precisely k_7 is set to k_4 + |
hidden |
boolean whether the variables H1 and H2 should be removed from output. |
ode.solver |
string specifying which ODE solver to use when
solving ODE. Should be one of the methods from the
|
seed |
random seed. Does not work if a "Detected blow-up" warning shows up. |
silent |
set to TRUE if no status output should be produced. |
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. |
simulated.model |
object returned by ODE solver. |
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.targetmodel
allow to simulate ODE
data from two additional models.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | simulation.obj <- (env=rep(1:5, 3),
L=15,
par.noise=list(noise.sd=0.02,
only.target.noise=FALSE,
relativ=TRUE),
intervention="initial_blockreactions",
intervention.par=0.1)
D <- simulation.obj$simulated.data
fulldata <- simulation.obj$simulated.model
time <- simulation.obj$time
plot(fulldata[[1]][,1], fulldata[[1]][,2], type= "l", lty=2,
xlab="time", ylab="concentration")
points(time, D[1,1:length(time)], col="red", pch=19)
legend("topright", c("true trajectory", "observations"),
col=c("black", "red"), lty=c(2, NA), pch=c(NA, 19))
|
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