estimate-mixedRegression-method: Estimation for the hierarchical (mixed) regression model

In BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

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})).

Usage

## S4 method for signature 'mixedRegression'
estimate(model.class, t, data, nMCMC, propSd,
  adapt = TRUE, proposal = c("normal", "lognormal"))

Arguments

model.class	class of the hierarchical regression model including all required information, see mixedRegression-class
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)

References

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.

Examples

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)

BaPreStoPro documentation built on May 2, 2019, 3:34 p.m.