. use "chd.dta"
. gen logpyrs=log(pyrs)
. glm chd smoke, family(poisson) link(log) offset(logpyrs)
Iteration 0: log likelihood = -84.1921
Iteration 1: log likelihood = -79.847801
Iteration 2: log likelihood = -79.844047
Iteration 3: log likelihood = -79.844047
Generalized linear models No. of obs = 16
Optimization : ML Residual df = 14
Scale parameter = 1
Deviance = 89.38192792 (1/df) Deviance = 6.384423
Pearson = 104.2445013 (1/df) Pearson = 7.446036
Variance function: V(u) = u [Poisson]
Link function : g(u) = ln(u) [Log]
AIC = 10.23051
Log likelihood = -79.84404706 BIC = 50.56569
------------------------------------------------------------------------------
| OIM
chd | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
smoke | .0317543 .0056242 5.65 0.000 .0207311 .0427775
_cons | -4.799334 .08852 -54.22 0.000 -4.97283 -4.625838
logpyrs | (offset)
------------------------------------------------------------------------------
Loglinear (Poisson) Regression Model of CHD on Smoking , Behavior Type and Blood Pressure
. glm chd smoke behavior bp,family(poisson) link(log) offset(logpyrs)
Iteration 0: log likelihood = -46.7864
Iteration 1: log likelihood = -45.775549
Iteration 2: log likelihood = -45.772957
Iteration 3: log likelihood = -45.772957
Generalized linear models No. of obs = 16
Optimization : ML Residual df = 12
Scale parameter = 1
Deviance = 21.239747 (1/df) Deviance = 1.769979
Pearson = 22.14547393 (1/df) Pearson = 1.845456
Variance function: V(u) = u [Poisson]
Link function : g(u) = ln(u) [Log]
AIC = 6.22162
Log likelihood = -45.77295661 BIC = -12.03132
------------------------------------------------------------------------------
| OIM
chd | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
smoke | .0273441 .0056142 4.87 0.000 .0163404 .0383477
behavior | .7525546 .136202 5.53 0.000 .4856035 1.019506
bp | .7533765 .1292403 5.83 0.000 .5000702 1.006683
_cons | -5.420153 .1308135 -41.43 0.000 -5.676543 -5.163763
logpyrs | (offset)
------------------------------------------------------------------------------