Bayesianbetareg (version 1.2)

gammakernel: the probability of a gamma parameter from the probability density funcion defined by old parameters

Description

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

Usage

gammakernel(X, Z, Y, gammas.n, betas.v, gammas.v, 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 variable

gammas.n

a vector with the gamma parameter - new parameters - 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

gpri

a vector with the initial values of gamma

Gpri

a matrix with the initial values of the variance of gamma

Value

value

a vector with the probability for the gamma parameter from the probability density function defined by old parameters

Details

Evaluate the probability of a gamma 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 Unpublished Ph.D. tesis.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

# Modelation of the gini coeficient with multiples variables

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)
gammas.n=c(0,0)
betas.v=c(0,0,0,0)
gammas.v=c(0,0)
gpri=c(0,0)
Gpri=diag(10,nrow=ncol(Z),ncol=ncol(Z))

dengamma <- gammakernel(X,Z,Y,gammas.n,betas.v,gammas.v,gpri,Gpri)
dengamma