Bayesianbetareg (version 1.2)

mukernel: the probability of a beta parameter from the probability density funcion defined by old parameters

Description

evaluate the probability of a beta parameter from the probability density function defined by old parameters

Usage

mukernel(X, Z, Y, betas.n, betas.v, gammas.v, 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 dependent variable

betas.n

a vector with the beta parameter, new parameter, to evaluate in the old p.d.f

betas.v

a vector with the beta that define the old p.d.f

gammas.v

a vector with the gamma that define the old p.d.f

bpri

a vector with the initial values of gamma

Bpri

a matrix with the initial values of the variance of gamma

Value

value

a matrix with the probability for the beta parameter from the probability density function defined by old parameters

Details

Evaluate the probability of a beta parameter from the probability density function defined by old parameters, according with 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 Unpublished Ph.D. tesis.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.n=c(0,0,0,0)
betas.v=c(0,0,0,0)
gammas.v=c(0,0)
bpri=c(0,0,0,0)
Bpri=diag(100,nrow=ncol(X),ncol=ncol(X))

denbeta <- mukernel(X,Z,Y,betas.n,betas.v,gammas.v,bpri,Bpri)
denbeta