simulate-jumpRegression-method: Simulation of regression model dependent on Poisson process

In BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Simulation of of the regression model y_i = f(t_i, N_{t_i}, θ) + ε_i with N_t\sim Pois(Λ(t, ξ)), ε_i\sim N(0,γ^2\widetilde{s}(t)).

Usage

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

Arguments

object	class object of parameters: "jumpRegression"
nsim	number of trajectories to simulate. Default is 1.
seed	optional: seed number for random number generator
t	vector of time points
plot.series	logical(1), if TRUE, simulated series are depicted grafically

Examples

model <- set.to.class("jumpRegression", fun = function(t, N, theta) theta[1]*t + theta[2]*N,
   parameter = list(theta = c(1,2), gamma2 = 0.1, xi = 10))
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t)

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