Description Usage Arguments References Examples
Bayesian estimation of the parameter of the hierarchical regression 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})).
1 2 3 |
model.class |
class of the hierarchical regression model including all required information, see |
t |
list or vector of time points |
data |
list or matrix of observation variables |
nMCMC |
length of Markov chain |
propSd |
vector of proposal variances for φ |
adapt |
if TRUE (default), proposal variance is adapted |
proposal |
proposal density: "normal" (default) or "lognormal" (for positive parameters) |
Hermann, S., K. Ickstadt, and C. H. Mueller (2016). Bayesian Prediction of Crack Growth Based on a Hierarchical Diffusion Model. Applied Stochastic Models in Business and Industry, DOI: 10.1002/asmb.2175.
1 2 3 4 5 6 7 8 9 | mu <- c(10, 5); Omega <- c(0.9, 0.01)
phi <- cbind(rnorm(21, mu[1], sqrt(Omega[1])), rnorm(21, mu[2], sqrt(Omega[2])))
model <- set.to.class("mixedRegression",
parameter = list(phi = phi, mu = mu, Omega = Omega, gamma2 = 0.1),
fun = function(phi, t) phi[1]*t + phi[2], sT.fun = function(t) 1)
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t, plot.series = FALSE)
est <- estimate(model, t, data[1:20,], 1000)
plot(est)
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