Description Usage Arguments Value Author(s) References Examples
View source: R/betaresiduals.R
This function calculates the beta regression residuals
1 | betaresiduals(Y,X, model)
|
Y |
object of class matrix, with the dependent variable |
X |
object of class matrix, with the independent variable |
model |
object of class Bayesianbetareg |
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 |
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. 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 Unpublished Ph.D. tesis.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.
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 | # 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)
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