ode.solver: ode.solver
In CausalKinetiX: Learning Stable Structures in Kinetic Systems

Description Usage Arguments Details Value Author(s) References Examples

Solves a mass-action ODE for a target variable Y by using smooth approximations of the predictor variables X.

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ode.solver(time_vec, initial_value, times, X, model, target, coefs,
  smooth.type = "smoothing.spline", reltol = 10^(-10),
  abstol = 10^(-16))

`time_vec`	numeric vector. Specifices the points at which to evaluate the target trajectory.
`initial_value`	numeric value. Specifies the value of the target at initial time point.
`times`	numeric vector of length L. Specifices the time points at which the predictors where observed.
`X`	predictor matrix of dimension L x d. Each column corresponds to a different predictor observed at the time points `times`.
`model`	list of mass-action terms. Each element in the list consists of a vector of predictor variables which are multiplied to a single term in the mass-action equation.
`target`	integer specifing which variable is the target.
`coefs`	numeric vector. Specifies the parameter values for each term in `model`.
`smooth.type`	string. Specifies which type of smoothing to use. The following options exist: "smoothing.spline", "loess", "linear", "constant".
`reltol`	numeric. Relative tolarance used in CVODE.
`abstol`	numeric. Absolute tolarance used in CVODE.

For further details see the references.

object returned by CVODE.

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).

## Generate data from Maillard reaction
simulation.obj <- generate.data.maillard(target=11,
                                         env=rep(1:5, each=5),
                                         L=20,
                                         par.noise=list(noise.sd=0.1,
                                                        only.target.noise=FALSE,
                                                        relativ=TRUE))
D <- simulation.obj$simulated.data
time <- simulation.obj$time

## Solve for Melanoidin
X <- do.call(cbind, split(as.vector(t(D[1:5,])), rep(1:11, each=length(unique(time)))))
times <- rep(unique(time), 5)
odefit <- ode.solver(time, 0, times, X, list(c(8)), 11, 0.12514)
plot(odefit[,1], odefit[,2], type="l")
points(times, X[,11])