Informations of model y_{ij} = f(φ_j, t_{ij}) + ε_{ij}, φ_j\sim N(μ, Ω), ε_{ij}\sim N(0,γ^2\widetilde{s}(t_{ij})).
phi
parameter φ
mu
parameter μ
Omega
parameter Ω
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
1 2 3 4 5 6 7 8 9 10 | 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 = 0.01)
fun <- function(phi, t) phi[1] + phi[2]*t
sT.fun <- function(t) t
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)
start <- parameter
model <- set.to.class("mixedRegression", parameter, prior, start, fun = fun, sT.fun = sT.fun)
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