Bayesianbetareg: Bayesianbetareg

In Bayesianbetareg: Bayesian Beta regression: joint mean and precision modeling

Description

Function to do Bayesian Beta Regression: joint mean and precision modeling

Usage

Bayesianbetareg(Y, X, Z, nsim, bpri, Bpri, gpri, Gpri, burn, jump, graph1, graph2)

Arguments

Y	object of class matrix, with the dependent variable.
X	object of class matrix, with the variables for modelling the mean.
Z	object of class matrix, with the variables for modelling the precision.
nsim	a number that indicate the number of iterations.
bpri	a vector with the initial values of beta.
Bpri	a matrix with the initial values of the variance of beta.
gpri	a vector with the initial values of gamma.
Gpri	a matrix with the initial values of the variance of gamma.
burn	a proportion that indicate the number of iterations to be burn at the beginning of the chain.
jump	a number that indicate the distance between samples of the autocorrelated the chain, to be excluded from the final chain.
graph1	if it is TRUE present the graph of the chains without jump and burn.
graph2	if it is TRUE present the graph of the chains with jump and burn.

Details

The bayesian beta regression allow the joint modelling of mean and precision of a beta distributed variable, as is proposed in Cepeda (2001), with logit link for the mean and logarithmic for the precision.

Value

object of class bayesbetareg with:

coefficients	object of class matrix with the estimated coefficients of beta and gamma.
desv	object of class matrix with the estimated desviations of beta and gamma.
interv	object of class matrix with the estimated confidence intervals of beta and gamma.
fitted.values	object of class matrix with the fitted values of y.
residuals	object of class matrix with the residuals of the regression.
precision	object of class matrix with the precision terms of the regression.
variance	object of class matrix with the variance terms of the regression.
beta.mcmc	object of class matrix with the complete chains for beta.
gamma.mcmc	object of class matrix with the complete chains for gamma.
beta.mcmc.short	object of class matrix with the chains for beta after the burned process.
gamma.mcmc.short	object of class matrix with the chains for gamma after the burned process.
call	Call.

Author(s)

Daniel Jaimes [e-mail], Margarita Marin [e-mail], Javier Rojas [e-mail], Hugo Andres Gutierrez Rojas [e-mail], Martha Corrales [e-mail], Maria Fernanda Zarate [e-mail], Ricardo Duplat [e-mail], Luis Villaraga [e-mail], Edilberto Cepeda-Cuervo [e-mail]

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/MODELAGEM20DA20VARIABILIDADE.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. // 3.Cepeda, E. and Garrido, L. (2011). Bayesian beta regression models: joint mean and precision modeling. Universidad Nacional // 4.Cepeda, E. and Migon, H. and Garrido, L. and Achcar, J. (2012) Generalized Linear models with random effects in the two parameter exponential family. Journal of Statistical Computation and Simulation. 1, 1 13.

Bayesianbetareg documentation built on May 30, 2017, 2:35 a.m.