rm(list = ls())
abt <- read.table(
url("http://stat.unm.edu/~fletcher/LLM/DATA/TAB3-1.DAT"),
#"C:\\E-drive\\Books\\ANREG2\\newdata\\TAB21-4.DAT",
#"C:\\E-drive\\Books\\LOGLIN3\\DATA\\TAB3-1.DAT",
		  sep="",col.names=c("R","S","A","O","y"))
attach(abt)
abt
#summary(abt)

r=factor(R)
o=factor(O)
s=factor(S)
a=factor(A)
A2=A*A
#[RSO][OA]
ab <- glm(y ~ r:s:o + o:a ,family = poisson)
abp=summary(ab)
abp
anova(ab)

#[RSO][A][O_1][O_2]
ab2 <- glm(y ~ r:s:o + a + o:A + o:A2,family = poisson)
abp2=summary(ab2)
abp2
anova(ab2)

#[RSO][A][O_1]
ab3 <- glm(y ~ r:s:o + a + o:A ,family = poisson)
abp3=summary(ab3)
abp3
anova(ab3)

rpearson=(y-ab3$fit)/(ab3$fit)^(.5)
rstand=rpearson/(1-hatvalues(ab3))^(.5)
infv = c(y,ab3$fit,hatvalues(ab3),rpearson,
		  rstand,cooks.distance(ab3))
inf=matrix(infv,I(abp3$df[1]+abp3$df[2]),6,dimnames =
list(NULL,c("y", "yhat", "lev","Pearson","Stand.","C")))
inf