simulate-hiddenmixedDiffusion-method: Simulation of hierarchical (mixed) hidden diffusion model

In BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Simulation of a stochastic process Z_{ij} = Y_{t_{ij}} + ε_{ij}, dY_t = b(φ_j,t,Y_t)dt + γ \widetilde{s}(t,Y_t)dW_t, φ_j\sim N(μ, Ω), Y_{t_0}=y_0(φ, t_0), ε_{ij}\sim N(0,σ^2).

Usage

## S4 method for signature 'hiddenmixedDiffusion'
simulate(object, nsim = 1, seed = NULL, t,
  mw = 10, plot.series = TRUE)

Arguments

object	class object of parameters: "hiddenmixedDiffusion"
nsim	number of data sets to simulate. Default is 1.
seed	optional: seed number for random number generator
t	vector of time points
mw	mesh width for finer Euler approximation to simulate time-continuity
plot.series	logical(1), if TRUE, simulated series are depicted grafically

Examples

mu <- c(5, 1); Omega <- c(0.9, 0.04)
phi <- cbind(rnorm(21, mu[1], sqrt(Omega[1])), rnorm(21, mu[2], sqrt(Omega[2])))
y0.fun <- function(phi, t) phi[2]
model <- set.to.class("hiddenmixedDiffusion", y0.fun = y0.fun,
   b.fun = function(phi, t, y) phi[1],
   parameter = list(phi = phi, mu = mu, Omega = Omega, gamma2 = 1, sigma2 = 0.01))
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t)

BaPreStoPro documentation built on May 2, 2019, 3:34 p.m.