rm(list = ls())
abt <- read.table(
url("http://stat.unm.edu/~fletcher/LLM/DATA/Example7-1-1.DAT"),
#"C:\\E-drive\\Books\\ANREG2\\newdata\\EX21-5-1.DAT",
#"C:\\E-drive\\Books\\LOGLIN3\\DATA\\Example7-1-1.dat",
		  sep="",col.names=c("c","p","y"))
attach(abt)
abt

C=factor(c)
P=factor(p)
m3 <- glm(y~C+P+C:p,family=poisson) #[C][P][C_1]
m2 <- glm(y~C+P+c:P,family=poisson) #[C][P][P_1]
m1 <- glm(y~C+P+c:p,family=poisson) #[C][P][gamma]
m0 <- glm(y~C+P,family=poisson)     #[C][P]
df=c(m3$df.residual,m2$df.residual,m1$df.residual,
     m0$df.residual)
G2=c(m3$deviance,m2$deviance,m1$deviance,m0$deviance)
A2q=G2-(2*df)
modelm=c(df,G2,A2q)
model=matrix(modelm,4,3,dimnames =
     list(NULL,c("df","G2","A-q")))
model

m1s=summary(m1)
m1s
anova(m1)

rpearson=(y-m1$fit)/(m1$fit)^(.5)
rstand=rpearson/(1-hatvalues(m1))^(.5)
infv = c(y,m1$fit,hatvalues(m1),rpearson,rstand,
   cooks.distance(m1))
inf=matrix(infv,I(m1s$df[1]+m1s$df[2]),6,dimnames =
list(NULL,c("y","yhat","lev","Pearson","Stand.","C")))
inf

m0$fit