jumpRegression-class {BaPreStoPro} | R Documentation |
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)).
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
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)