estSDE: Bayesian estimation in mixed stochastic differential...
In SimoneHermann/hierRegSDE:

Description Usage Arguments Details Value References Examples

Bayesian estimation of the random effects φ_j in the mixed SDE dY_i(t)= b(φ_i, t, Y_i(t))dt + γ s(t, Y_i(t)) dW_i(t), , φ_i~N(μ, Ω), i=1,...,n and the parameters μ, Ω, γ^2.

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estSDE(t, y, prior, start, bSDE, sVar, ipred = 1, cut, len = 1000,
  Omega = "diag", mod = c("Gompertz", "logistic", "Weibull", "Richards",
  "Paris", "Paris2"), modVar = "", propPar = 0.02)

`t`	vector of observation times
`y`	matrix or list of the n trajectories
`prior`	list of prior parameters - list(m, v, priorOmega, alpha, beta), priorOmega=list(alpha, beta) if Omega="diag", otherwise prior matrix of Wishart distribution
`start`	list of starting values
`bSDE`	b(phi, t, x) drift function
`sVar`	variance function s^2
`ipred`	which of the n trajectories is the one to be predicted
`cut`	the index how many of the ipred-th series are used for estimation
`len`	number of iterations of the MCMC algorithm - chain length
`Omega`	structure of the variance matrix Omega of the random effects, diagonal matrix, otherwise inverse wishart distributed
`mod`	model out of Gompertz, Richards, logistic, Weibull, Paris, Paris2, only used instead of bSDE
`modVar`	default value is sVar(t,x)=1, if "AR": sVar(t,x)=x
`propPar`	proposal standard deviation of phi is \|start$mu\|*propPar

Simulation from the posterior distribution of the random effect from n independent trajectories of the SDE (the Brownian motions W1,...,Wn are independent).

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`phi`	samples from posterior of φ
`mu`	samples from posterior of μ
`Omega`	samples from posterior of Ω
`gamma2`	samples from posterior of γ^2

Hermann et al. (2015)

mod <- "Gompertz"
bSDE <- getFun("SDE", mod)
mu <- getPar("SDE", mod, "truePar")
n <- 5
parameters <- defaultPar(mu, n)
y <- drawData("SDE", bSDE, parameters)
t <- parameters$t

prior <- getPrior(mu, parameters$gamma2)
start <- getStart(mu, n)
chains <- estSDE(t, y, prior, start, bSDE)