rm(list = ls())
cnt=c(34,31,19,23,61,19,23,39,16,17,16,12)
pa=c(1,1,1,1,2,2,2,2,3,3,3,3)
clg=c(1,2,3,4,1,2,3,4,1,2,3,4)

# R assumes pa and clg contain real numbers.  For glm,
# we need to specify them as integers using "factor".
PA=factor(pa)
CLG=factor(clg)

# Run glm and obtain summary tables
ts <- glm(cnt ~ PA + CLG,family = poisson)
tsp=summary(ts)
tsp
anova(ts)

# Constructing additional output
rpearson=(cnt-ts$fit)/(ts$fit)^(.5)
# or rpearson = residuals(ts,type="pearson")
rstand=rpearson/(1-hatvalues(ts))^(.5)
infv = c(cnt,ts$fit,hatvalues(ts),rpearson,
		  rstand,cooks.distance(ts))
inf=matrix(infv,I(tsp$df[1]+tsp$df[2]),6,dimnames =
list(NULL,c("n","mhat","lev","Pearson","Stand.","C")))
inf

fitted(ts)
df.residual(ts)
sum(residuals(ts,type="pearson")^2)
deviance(ts)
residuals(ts,type="pearson")