jumpRegression-class: S4 class of model informations for the jump regression model

In BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Informations of 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)).

Slots

theta

parameter θ

gamma2

parameter γ^2

xi

parameter ξ

fun

function f(t, N, θ)

sT.fun

function \widetilde{s}(t)

Lambda

function Λ(t,ξ)

prior

list of prior parameters

start

list of starting values for the Metropolis within Gibbs sampler

Examples

parameter <- list(theta = c(3, 1), gamma2 = 0.1, xi = c(2, 0.2))
fun <- function(t, N, theta) theta[1]*t + theta[2]*N
sT.fun <- function(t) t
Lambda <- function(t, xi) (t / xi[2])^xi[1]
prior <- list(m.theta = parameter$theta, v.theta = parameter$theta^2,
   alpha.gamma = 3, beta.gamma = parameter$gamma2*2)
start <- parameter
model <- set.to.class("jumpRegression", parameter, prior, start = start,
  fun = fun, sT.fun = sT.fun, Lambda = Lambda)

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