diagnostics: Plot the residuals of the bayesian beta regression

Description Usage Arguments Value Author(s) Examples

View source: R/diagnostics.R

Description

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

Usage

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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 dajaimesc@unal.edu.co, Margarita Marin mmarinj@unal.edu.co, Javier Rojas jarojasag@unal.edu.co, Martha Corrales martha.corrales@usa.edu.co, Maria Fernanda Zarate mfzaratej@unal.edu.co, Ricardo Duplat rrduplatd@unal.edu.co, Luis Villaraga lfvillarragap@unal.edu.co, Edilberto Cepeda-Cuervo ecepedac@unal.edu.co

Examples

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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.