estimate-Regression-method: Estimation for regression model

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Bayesian estimation of the parameter of the regression model y_i = f(φ, t_i) + ε_i, ε_i\sim N(0,γ^2\widetilde{s}(t_i)).

Usage

## S4 method for signature 'Regression'
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 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

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)

SimoneHermann/BaPreStoPro documentation built on May 9, 2019, 1:46 p.m.