Regression-class {BaPreStoPro} | R Documentation |
Informations of model y_i = f(φ, t_i) + ε_i, ε_i\sim N(0,γ^2\widetilde{s}(t_i)).
phi
parameter φ
gamma2
parameter γ^2
fun
function f(φ, t)
sT.fun
function \widetilde{s}(t)
prior
list of prior parameters
start
list of starting values for the Metropolis within Gibbs sampler
parameter <- list(phi = c(3, 1), gamma2 = 0.1) fun <- function(phi, t) phi[1] + phi[2]*t sT.fun <- function(t) t prior <- list(m.phi = parameter$phi, v.phi = parameter$phi^2, alpha.gamma = 3, beta.gamma = 2*parameter$gamma2) start <- parameter model <- set.to.class("Regression", parameter, prior, start, fun = fun, sT.fun = sT.fun)