rm(list = ls())
abop <- read.table(
url("http://stat.unm.edu/~fletcher/LLM/DATA/TAB3-1-logistic.DAT"),
#"C:\\E-drive\\Books\\ANREG2\\newdata\\tab20-15.dat",
#"C:\\E-drive\\Books\\LOGLIN3\\DATA\\tab3-1-logistic.dat",
sep="",col.names=
c("Case","Race","Sex","Age","Yes","No","Total","Und"))
attach(abop)
abop
#summary(abop)

#Summary tables
R=factor(Race)
S=factor(Sex)
A=factor(Age)
y=Yes/Total
# Model (4.6.5)
ab <- glm(y~R:S+A,family=binomial,weights=Total)
abp=summary(ab)
abp

odds=ab$fit/(1-ab$fit)
odds

# Model (4.6.6)
ab6 <- glm(y~R:S+Age,family=binomial,weights=Total)
abp=summary(ab6)
abp
anova(ab6,ab5)

# Model (4.6.7)
Men=Race*(Sex-1)
m=factor(Men)
ab7 <- glm(y~m+A,family=binomial,weights=Total)
abp=summary(ab7)
abp
anova(ab7,ab5)

# Model (4.6.8)
ab8 <- glm(y~m+Age,family=binomial,weights=Total)
abp=summary(ab8)
abp
anova(ab8,ab5)

odds=ab8$fit/(1-ab8$fit)
oddstable=matrix(odds,6,4,dimnames =
list(NULL,c("Male", "   White Female",
"      Male","   Nonwhite Female")))
oddstable