Regression-class: S4 class of model informations for the regression model

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Informations of model y_i = f(φ, t_i) + ε_i, ε_i\sim N(0,γ^2\widetilde{s}(t_i)).

Slots

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

Examples

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)

SimoneHermann/BaPreStoPro documentation built on May 9, 2019, 1:46 p.m.