BaPreStoPro (version 0.1)

estimate: Bayesian estimation

Description

Estimation method for the S4 classes.

Usage

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

Arguments

model.class

class object with model informations, see set.to.class

t

vector or list of time points

data

vector or list or matrix of observation variables

nMCMC

length of Markov chain

propSd

vector of proposal variances

adapt

if TRUE (default), proposal variance is adapted

proposal

proposal density: "normal" (default) or "lognormal" (for positive parameters)

...

parameters dependent on the model class

Value

class object est.model.class containing Markov chains, data input and model informations

References

Hermann, S. (2016). BaPreStoPro: an R Package for Bayesian Prediction of Stochastic Processes. SFB 823 discussion paper 28/16.

Robert, C. P. and G. Casella (2004). Monte Carlo Statistical Methods. Springer, New York.

Rosenthal, J. S. (2011). Optimal Proposal Distributions and Adaptive MCMC. In: Handbook of Markov Chain Monte Carlo, pp. 93-112.