estimate,Regression-method {BaPreStoPro} | R Documentation |
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)).
## S4 method for signature 'Regression' estimate(model.class, t, data, nMCMC, propSd, adapt = TRUE, proposal = c("normal", "lognormal"))
model.class |
class of the regression model including all required information, see |
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) |
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.
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)