dias<-c(21,24,25,26,28,31,33,34,35,37,43,49,51,25,29,43,44,46,46,51,55,56,58,55)
sexo<-rep(c(1,0),12)
resp<-c(rep(1,12),rep(0,12))
sexo<-factor(sexo)
sexo<-C(sexo,treatment)
fit=glm(resp~sexo+dias,family=binomial)
summary(fit)
fit0=glm(resp~dias,family=binomial)
anova(fit0,fit,test="Chi")
fit=fit0
predict(fit)
fitted(fit)
yh=fitted(fit)
X=model.matrix(fit)
w=fit$weights
W=diag(w)
H=sqrt(W)%*%X%*%solve(t(X)%*%W%*%X)%*%t(X)%*%sqrt(W)
h=diag(H)
plot(h,xlab="Índice")
identify(h)
ro=residuals(fit,type="response")
fi=1
rd=residuals(fit,type="deviance")
td=rd*sqrt(fi/(1-h))
rp=residuals(fit,type="pearson")
rp=sqrt(fi)*rp
ts=rp/sqrt(1-h)
LD=h*(ts^2)/(1-h)
plot(td,ylab="Componente do desvio",xlab="Índice")
identify(td)
plot(LD,ylab="Distância de Cook",xlab="Índice")
identify(LD)
curve(exp(5.7162-0.1454*x)/(1+exp(5.7162-0.1454*x)) , 0, 60)