dnsrposoba {OBASpatial} | R Documentation |
Objective posterior density for the NSR model
Description
It calculates the density function (up to a proportionality constant) for the TSR model using the based reference, Jeffreys' rule, Jeffreys' independent and vague priors. In this context
corresponds to the range parameter.
Usage
dnsrposoba(x,formula,prior="reference",coords.col=1:2,
kappa=0.5,cov.model="exponential",data,asigma=2.1,intphi)
Arguments
x |
The |
formula |
A valid formula for a linear regression model. |
prior |
Objective prior densities avaiable for the TSR model: ( |
coords.col |
A vector with the column numbers corresponding to the spatial coordinates. |
kappa |
Shape parameter of the covariance function (fixed). |
cov.model |
Covariance functions available for the TSR
model. |
data |
Data set with 2D spatial coordinates, the response and optional covariates. |
asigma |
Value of |
intphi |
An interval for |
Details
The posterior distribution is computed for this priors under the improper family . For the vague prior, it was considered the structure where a priori,
folows an uniform distribution on the interval
intphi
.
For the Jeffreys independent prior, this family of priors generates improper posterior distribution when intercept is considered for the mean function.
Value
Posterior density of x=.
Author(s)
Jose A. Ordonez, Marcos O. Prates, Larissa A. Matos, Victor H. Lachos.
References
Berger, J.O, De Oliveira, V. and Sanso, B. (2001). Objective Bayesian Analysis of Spatially Correlated Data. Journal of the American Statistical Association., 96, 1361 – 1374.
See Also
dtsrposoba
,dtsrprioroba
,dnsrprioroba
Examples
data(dataelev)
######### Using reference prior ###########
dnsrposoba(x=5,prior="reference",formula=elevation~1,
kappa=1,cov.model="matern",data=dataelev)
######### Using Jeffreys' rule prior ###########
dnsrposoba(x=5,prior="jef.rul",formula=elevation~1,
kappa=1,cov.model="matern",data=dataelev)
######### Using vague independent prior ###########
dnsrposoba(x=5,prior="vague",formula=elevation~1,
kappa=0.3,cov.model="matern",data=dataelev,intphi=c(0.1,10))