View source: R/prinfunctions.R
dnsrposoba | R Documentation |
It calculates the density function π(φ) (up to a proportionality constant) for the TSR model using the based reference, Jeffreys' rule, Jeffreys' independent and vague priors. In this context φ corresponds to the range parameter.
dnsrposoba(x,formula,prior="reference",coords.col=1:2, kappa=0.5,cov.model="exponential",data,asigma=2.1,intphi)
x |
The φ quantil value. |
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 a for vague prior. |
intphi |
An interval for φ used for vague prior. |
The posterior distribution is computed for this priors under the improper family \frac{π(φ)}{(σ^2)^a}. For the vague prior, it was considered the structure where a priori, φ 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.
Posterior density of x=φ.
Jose A. Ordonez, Marcos O. Prates, Larissa A. Matos, Victor H. Lachos.
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.
dtsrposoba
,dtsrprioroba
,dnsrprioroba
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))
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