Bayesianbetareg (version 1.2)

dpostb: Posterior value of beta

Description

Propose a value for posterior distribution of the beta parameter

Usage

dpostb(X, Z, Y, betas, gammas, bpri, Bpri)

Arguments

X

object of class matrix, with the variables for modelling the mean

Z

object of class matrix, with the variables for modelling the variance

Y

object of class matrix, with the dependen variables

betas

a vector with the previous proposal beta parameters

gammas

a vector with the previous proposal gamma parameters

bpri

a vector with the initial values of beta

Bpri

a matrix with the initial values of the variance of beta

Value

value

a matrix with the proposal for beta

Details

Generate a proposal for the beta parameter according to the model proposed by Cepeda and Gamerman(2005).

References

1. Cepeda C. E. (2001). Modelagem da variabilidade em modelos lineares generalizados. Unpublished Ph.D. tesis. Instituto de Matematicas. Universidade Federal do Rio do Janeiro. //http://www.docentes.unal.edu.co/ecepedac/docs/MODELAGEMDAVARIABILIDADE.pdf. http://www.bdigital.unal.edu.co/9394/. 2.Cepeda, E. C. and Gamerman D. (2005). Bayesian Methodology for modeling parameters in the two parameter exponential family. Estadistica 57, 93 105.

Examples

library(betareg)
data(ReadingSkills)


Y <- as.matrix(ReadingSkills[,1])
n <- length(Y)
X1 <- as.matrix(ReadingSkills[,2])
for(i in 1:length(X1)){
  X1 <- replace(X1,X1=="yes",1)
  X1 <- replace(X1,X1=="no",0)
}
X0 <- rep(1, times=n)
X1 <- as.numeric(X1)
X2 <- as.matrix(ReadingSkills[,3])
X3 <- X1*X2
X <- cbind(X0,X1,X2,X3)
Z0 <-  X0 
Z <- cbind(X0,X1)
betas.ind=c(0,0,0,0)
gammas.ind=c(0,0)
bpri=c(0,0,0,0)
Bpri=diag(10,nrow=ncol(X),ncol=ncol(X))

beta <- dpostb(X,Z,Y,betas.ind,gammas.ind,bpri,Bpri)
beta