#-----------------------------------------------------------------------#
# Comandos:
#          fit.model = ajuste
#          attach(dados)
#          source("diag_quase.txt")
#-----------------------------------------------------------------------#
par(mfrow=c(1,2))
X =  model.matrix(fit.model)
n =  nrow(X)
p =  ncol(X)
w =  fit.model$weights
W =  diag(w)
H =  solve(t(X)%*%W%*%X)
H =  sqrt(W)%*%X%*%H%*%t(X)%*%sqrt(W)
h =  diag(H)
sigma2 =  summary(fit.model)$dispersion
ts =  resid(fit.model,type="pearson")/sqrt(sigma2)
di =  (h/((1-h)*2))*(ts^2)
a =  min(td)
b =  max(td)
#
plot(di,xlab="Indice", ylab="Distancia de Cook", pch=16, cex=2, cex.axis=1.5,
cex.lab=1.5)
#identify(di, n=1,cex=2)
#
plot(predict(fit.model), ts,xlab="Preditor Linear", ylab="Residuo de Pearson",
ylim=c(a-1,b+1), pch=16)
abline(2,0,lty=2,lwd=2)
abline(-2,0,lty=2,lwd=2)
#identify(fitted(fit.model), ts, n=1,cex=2)
#-----------------------------------------------------------------------#