Description Usage Arguments Value Author(s) Examples
Plot the residuals (pearson standarized and deviance), the Cooks distance and the leverage against the predicted values for the Bayesian Beta Regression
1 | diagnostics(model, residuals)
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model |
object of class bayesbetareg, with the structure of the model |
residuals |
object of class bayesbetareg, with the residuals of the Bayesianbetareg |
Plot the residuals of the bayesian beta regression
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | 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)
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