BaPreStoPro (version 0.1)

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

Description

Informations of model $y_i = f(t_i, N_{t_i}, \theta) + \epsilon_i$ with $N_t\sim Pois(\Lambda(t, \xi)), \epsilon_i\sim N(0,\gamma^2\widetilde{s}(t))$.

Slots

theta

parameter $\theta$

gamma2

parameter $\gamma^2$

xi

parameter $\xi$

fun

function $f(t, N, \theta)$

sT.fun

function $\widetilde{s}(t)$

Lambda

function $\Lambda(t,\xi)$

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)