* Poisson Chapter
drop _all

clear
input LOS count
1  9
2  10
3  11
4  12
5  13
6  12
7  11
8  10
9  9
10  8
11  7
12  6
13  5
14  4
15  3
16  2
17  1
end
capture program drop doit
program define doit
	version 6.0
	graph count LOS, xlab(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17) /*
		*/ ylab(0,1,2,3,4,5,6,7,8,9,10,11,12,13) c(.) s(o) saving(poisson, replace)
end
doit
graph export poisson.eps, replace


use medpar, clear
glm los hmo white type2 type3, family(poisson) eform nolog

glm los hmo white type2 type3, family(poisson) irls scale(dev) eform nolog noheader

glm los hmo white type2 type3, family(poisson) cluster(provnum) eform nolog noheader

use doll, clear
glm deaths smokes a2-a5, family(poisson) lnoffset(pyears) eform nolog

glm deaths smokes a2-a5, family(poisson) irls scale(dev) lnoffset(pyears) eform nolog noheader

gen sa12 = smokes*(a1+a2)
gen sa34 = smokes*(a3+a4)
gen sa5  = smokes*(a5)
glm deaths sa12 sa34 sa5 a2-a5, family(poisson) irls scale(dev) lnoffset(pyears) eform nolog noheader

use medpar, clear
glm los hmo white type2 type3, family(poisson) eform nolog noheader
predict double mu, mu
generate z = ((los-mu)^2-los) / (mu*sqrt(2))
regress z

quietly summarize los, meanonly
quietly scalar nybar = r(sum)
quietly generate double musq = mu*mu
quietly summarize musq, meanonly
quietly scalar mu2 = r(sum)
quietly scalar chival = (mu2-nybar)^2/(2*mu2)
display as txt "LM value = " as res chival _n as txt "P-value  = " as res %6.4f      chi2tail(1, chival)

sysuse cancer, clear
tab drug, gen(drug)
glm died age drug2 drug3, lnoffset(studytime) family(poisson) nolog
stset studytime died
streg age drug2 drug3, nohr dist(exp) nolog

glm died age drug2 drug3, lnoffset(studytime) family(poisson) nolog noheader

test age=0, notest
test drug2=-1.5, accumulate

estimates store Unconstrained

generate double offvar = 0*age - 1.5*drug2 + log(studytime)

glm died drug3, offset(offvar) family(poisson) nolog 
lrtest Unconstrained