BaPreStoPro (version 0.1)

estimate,Regression-method: Estimation for regression model

Description

Bayesian estimation of the parameter of the regression model $y_i = f(\phi, t_i) + \epsilon_i, \epsilon_i\sim N(0,\gamma^2\widetilde{s}(t_i))$.

Usage

"estimate"(model.class, t, data, nMCMC, propSd, adapt = TRUE, proposal = c("normal", "lognormal"))

Arguments

model.class

class of the regression model including all required information, see Regression-class

t

vector of time points

data

vector of observation variables

nMCMC

length of Markov chain

propSd

vector of proposal variances for $\phi$

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

t <- seq(0,1, by = 0.01)
model <- set.to.class("Regression", fun = function(phi, t) phi[1]*t + phi[2],
                   parameter = list(phi = c(1,2), gamma2 = 0.1))
data <- simulate(model, t = t, plot.series = TRUE)
est <- estimate(model, t, data, 1000)
plot(est)