# hiddenmixedDiffusion-class: S4 class of model informations for hierarchical (mixed)... In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

## Description

Informations of model Z_{ij} = Y_{t_{ij}} + ε_{ij}, dY_t = b(φ_j,t,Y_t)dt + γ \widetilde{s}(t,Y_t)dW_t, φ_j\sim N(μ, Ω), Y_{t_0}=y_0(φ, t_0), ε_{ij}\sim N(0,σ^2).

## Slots

`phi`

parameter φ

`mu`

parameter μ

`Omega`

parameter Ω

`gamma2`

parameter γ^2

`sigma2`

parameter σ^2

`y0.fun`

function y_0(φ, t)

`b.fun`

function b(φ,t,y)

`sT.fun`

function \widetilde{s}(t,y)

`prior`

list of prior parameters

`start`

list of starting values for the Metropolis within Gibbs sampler

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```mu <- c(2, 1); Omega <- c(1, 0.04) phi <- sapply(1:2, function(i) rnorm(21, mu[i], sqrt(Omega[i]))) parameter <- list(phi = phi, mu = mu, Omega = Omega, gamma2 = 0.1, sigma2 = 0.1) b.fun <- function(phi, t, y) phi[1] * y sT.fun <- function(t, y) y y0.fun <- function(phi, t) phi[2] start <- parameter prior <- list(m.mu = parameter\$mu, v.mu = parameter\$mu^2, alpha.omega = rep(3, length(parameter\$mu)), beta.omega = parameter\$Omega*2, alpha.gamma = 3, beta.gamma = parameter\$gamma2*2, alpha.sigma = 3, beta.sigma = parameter\$sigma2*2) model <- set.to.class("hiddenmixedDiffusion", parameter, prior, start, b.fun = b.fun, sT.fun = sT.fun, y0.fun = y0.fun) ```

SimoneHermann/BaPreStoPro documentation built on May 10, 2017, 1:42 p.m.