hbstm.fit: Fitted function for Hierarchical Bayesian Space Time models

In HBSTM: Hierarchical Bayesian Space-Time Models for Gaussian Space-Time Data

Fitted function for Hierarchical Bayesian Space Time models

Description

This is the basic computing engine that hbstm uses to fit Hierarchical Bayesian Space Time models. In general, this should not be used directly, unless by experienced users.

Usage

	hbstm.fit(HBSTM,nIter,nBurn,time,timerem,plots,posterior,save)

Arguments

HBSTM	An object of class "HBSTM".
nIter	Number of Gibbs Sampling iterations. Default value is 1000.
nBurn	Number of burn-in samples. This number of samples will be discarded before making any inference. Default value is the 20 percent of nIter.
time	A "logical" indicating whether the method shows the estimated time of execution.
timerem	A "logical" indicating whether the method shows the estimated remaining time of execution.
plots	A "logical" indicating whether the method shows the plots of the execution (the mse, Zt vs K*Yt and the ACF/PACF of the residuals

.

posterior	A "character" indicating whether the function returns the mean and the standard deviation of the fitted values of Yt or returns the median with its 95 percent credibility intervals.
save	A "character" indicating if, for each iteration, the algorithm save the estimation of certain parameters. See "Details" for more information.

Details

The save argument is a "character" that can have any of the following options:

-"all": Save an object of class Parameters.

-"Mu": Save an object of class Mu.

-"Mt": Save an object of class Mt.

-"Xt": Save an object of class Xt.

Value

hbstm.fit returns an object of class HBSTM

Author(s)

Pilar Munyoz and Alberto Lopez Moreno

See Also

Overview: HBSTM-package
Classes : HBSTM,Parameters,Mu,Mt,Xt,Autoregressive,Seas,SpatParam,VectSubdiag, Hyperpriors,Mu0,Mt0,Xt0,Seas0,Autoregressive0,SpatParam0,VectSubdiag0
Methods : hbstm,hbstm.fit,results,estimation,resid,mse
Plot : plotRes,plotFit
Data: hirlam,coordinates

Examples

## See 'tutorial.pdf', included in the documentation of the package, to see a full example

HBSTM documentation built on May 28, 2022, 5:05 p.m.