Fitting Hierarchical Bayesian Space Time models
hbstm
is used to fit Hierarchical Bayesian Space Time models.
hbstm(Zt,K,newGrid,reglag,seas,spatlags,hyperpriors,initialvalues, nIter,nBurn,fit,plots,posterior,save,control)
Zt |
: MxT |
K |
: MxS |
newGrid |
An Sx2 |
reglag |
A vector containing the temporal autoregressive lags of the model. |
seas |
A vector containing the seasonal coefficients of the model. |
spatlags |
A vector of length 4 containing the spatial lags of the model. See details for more information. |
hyperpriors |
An object of class |
initialvalues |
An object of class |
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. |
fit |
A |
plots |
A |
posterior |
A |
save |
A |
control |
a list of control parameters. See "Details". |
Each position of the argument spatlags
refers to the spatial lags of a specified direction. These four directions are "east-west", "north-south", "northwest-southeast" and "northeast-southwest".
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
.
The control
argument is a list that can supply any of the following components:
-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
-seed
: The seed to use in the function "set.seed"
and set it to fit the model.
hbstm
returns an object of class HBSTM
Pilar Munyoz and Alberto Lopez Moreno
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
## See 'tutorial.pdf', included in the documentation of the package, to see a full example
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