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

 ```1 2 3``` ```## 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

 ```1 2 3 4 5 6 7 8``` ```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) ```

SimoneHermann/BaPreStoPro documentation built on May 10, 2017, 1:42 p.m.