Description Usage Arguments Examples
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).
1 2 3 |
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 |
1 2 3 4 5 6 | 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)
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