diagnostics: Plot the residuals of the bayesian beta regression

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

Description

Plot the residuals (pearson standarized and deviance), the Cooks distance and the leverage against the predicted values for the Bayesian Beta Regression

Usage

diagnostics(model, residuals)

Arguments

model	object of class bayesbetareg, with the structure of the model
residuals	object of class bayesbetareg, with the residuals of the Bayesianbetareg

Value

Plot the residuals of the bayesian beta regression

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]

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)

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)

#Example of the function betasresiduals and plots

readingskillsresiduals<- betaresiduals(Y,X,re)
diagnostics(re,readingskillsresiduals)

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