Bayesianbetareg (version 1.2)

dpostg: Posterior value of gamma

Description

Propose a value for posterior distribution of the gamma parameter

Usage

dpostg(X, Z, Y, betas, gammas, gpri, Gpri)

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 dependent variables

betas

a vector with the previous proposal beta parameters

gammas

a vector with the previous proposal gamma parameters

gpri

a vector with the initial values of beta

Gpri

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(2001) and 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 Unpublished Ph.D. tesis.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)
Bpri=diag(10,nrow=ncol(Z),ncol=ncol(Z))

gamma <- dpostg(X,Z,Y,betas.ind,gammas.ind,bpri,Bpri)
gamma