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

In BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Simulation of the stochastic process model dY_t = b(φ_j,t,Y_t)dt + γ \widetilde{s}(t,Y_t)dW_t, φ_j~N(μ, Ω).

Usage

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

Arguments

object	class object of parameters: "mixedDiffusion"
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 <- 2; Omega <- 0.4; phi <- matrix(rnorm(21, mu, sqrt(Omega)))
model <- set.to.class("mixedDiffusion", y0.fun = function(phi, t) 0.5,
  parameter = list(phi = phi, mu = mu, Omega = Omega, gamma2 = 0.1),
  b.fun = function(phi, t, x) phi*x, sT.fun = function(t, x) x)
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t, plot.series = TRUE)

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