rm(list = ls())
oring.sllr <- read.table(
#"C:\\E-drive\\Books\\ANREG2\\newdata\\tab20-3.DAT",
#"C:\\E-drive\\Books\\LOGLIN3\\DATA\\tab2-1.dat",
url("http://stat.unm.edu/~fletcher/LLM/DATA/TAB2-1.dat"),
		  sep="",col.names=c("Case","Flt","y","s","f","no"))

attach(oring.sllr)
oring.sllr
#summary(oring.sllr)

#Summary tables
or <- glm(y ~ x,family = binomial)
orp=summary(or)
orp
anova(or)


#prediction
new = data.frame(x=c(31,53))
predict(or,new,type="response")
rpearson=(y-or$fit)/(or$fit*(1-or$fit))^(.5)
rstand=rpearson/(1-hatvalues(or))^(.5)
infv = c(y,or$fit,hatvalues(or),rpearson,
		  rstand,cooks.distance(or))
inf=matrix(infv,I(orp$df[1]+orp$df[2]),6,dimnames =
list(NULL,c("y","yhat","lev","Pearson","Stand.","C")))
inf
R2 = (cor(y,or$fit))^2
R2
altR2=(or$null.deviance - or$deviance)/or$null.deviance
altR2