betaresiduals: Residuals of the Beta Regression

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

Description

This function calculates the beta regression residuals

Usage

betaresiduals(Y,X, model)

Arguments

Y	object of class matrix, with the dependent variable
X	object of class matrix, with the independent variable
model	object of class Bayesianbetareg

Value

abs	The raw response residuals
swr0	Pearson residuals
swr1	standardized weighted residual 1
swr2	standardized weighted residual 2
deviance	deviance residuals
cook	cook residuals
H	H matrix H

Author(s)

Daniel Jaimes [e-mail], Margarita Marin [e-mail], Javier 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 Unpublished Ph.D. tesis.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.

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)

burn <- 0.3
jump <- 3
nsim <- 400

bpri <- c(0,0,0,0)
Bpri <- diag(100,nrow=ncol(X),ncol=ncol(X))
gpri <- c(0,0)
Gpri <- diag(10,nrow=ncol(Z),ncol=ncol(Z))

re<-Bayesianbetareg(Y,X,Z,nsim,bpri,Bpri,gpri,Gpri,0.3,3,graph1=FALSE,graph2=FALSE)
summary(re)
reading_skillsresiduals<- betaresiduals(Y,X,re)

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