AIC = Log likelihood = BIC =
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1 - log: /mnt/ide1/home/sschulh1/apc/apc_examplelog log type: text opened on: 21 Jul 2006, 18:08:20 *replicate table 5 and cols 7-9 of table 3 in Yang, Fu and Land (2004) *Stata can maximize GLM objective functions using two different numerical *optimization methods: Newton-Raphson (NR) and iterative reweighted least *squares (IRLS) NR is the default in Stata However, NR presents a *problem in replicating Yang, Fu and Land: The paper set the scale *parameter equal to the deviance divided by the residual degrees of *freedom, but Stata allows this scale parameter only with IRLS and not *with NR So, we show two sets of results below: * 1 NR optimization with scale parameter=pearson chi-squared/residual df * 2 IRLS optimization with scale parameter=deviance/residual df *Version 1 is basically the default in Stata Version 2 matches what was *done in the paper The results are numerically identical to the number of *decimal places shown in the paper use apc_example_datadta *first, using Newton-Raphson optimization (the default in Stata) and scale(x2 > ) * (scale parameter = Pearson chi-squared / residual degrees of freedom) #delim ; delimiter now ; apc_ie death_f if age<=90, > age(age) period(year) cohort(cohort) family(poisson) link(log) > exposure(exp_f) scale(x2); Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Iteration 5: log likelihood = Intrinsic estimator of APC effects No of obs = 152 Optimization : ML Residual df = 102 Scale parameter = 1 Deviance = (1/df) Deviance = Pearson = (1/df) Pearson = AIC = Log likelihood = BIC = OIM age_
2 age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ period_ period_ period_ period_ period_ period_ period_ period_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ _cons apc_cglim death_f if age<=90,
3 > age(age) period(year) cohort(cohort) > agepfx("_a") periodpfx("_p") cohortpfx("_c") > family(poisson) link(log) > exposure(exp_f) scale(x2) constraint("a5=a10"); Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Iteration 5: log likelihood = Generalized linear models No of obs = 152 Optimization : ML Residual df = 102 Scale parameter = 1 Deviance = (1/df) Deviance = Pearson = (1/df) Pearson = AIC = Log likelihood = BIC = OIM _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _P_ _P_ _P_ _P_ _P_ _P_ _P_ _C_ _C_ _C_ _C_
4 _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _cons (Standard errors scaled using square root of Pearson X2-based dispersion) _A_5=_A_10 drop _A* _P* _C*; apc_cglim death_f if age<=90, > age(age) period(year) cohort(cohort) > agepfx("_a") periodpfx("_p") cohortpfx("_c") > family(poisson) link(log) > exposure(exp_f) scale(x2) constraint("p1965=p1960"); Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Iteration 5: log likelihood = Generalized linear models No of obs = 152 Optimization : ML Residual df = 102 Scale parameter = 1 Deviance = (1/df) Deviance = Pearson = (1/df) Pearson = AIC = Log likelihood = BIC = OIM
5 _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _P_ _P_ _P_ _P_ _P_ _P_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _cons (Standard errors scaled using square root of Pearson X2-based dispersion) _P_1965=_P_1960
6 drop _A* _P* _C*; apc_cglim death_f if age<=90, > age(age) period(year) cohort(cohort) > agepfx("_a") periodpfx("_p") cohortpfx("_c") > family(poisson) link(log) > exposure(exp_f) scale(x2) constraint("c1995=c1990"); Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Iteration 5: log likelihood = Generalized linear models No of obs = 152 Optimization : ML Residual df = 102 Scale parameter = 1 Deviance = (1/df) Deviance = Pearson = (1/df) Pearson = AIC = Log likelihood = BIC = OIM _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _P_ _P_ _P_ _P_ _P_ _P_ _P_
7 _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _cons (Standard errors scaled using square root of Pearson X2-based dispersion) _C_1995=_C_1990 drop _A* _P* _C*; #delim cr delimiter now cr *next, using IRLS optimization (the default in S-Plus) and scale(dev) * (scale parameter = deviance / residual degrees of freedom) #delim ; delimiter now ; apc_ie death_f if age<=90, > age(age) period(year) cohort(cohort) family(poisson) link(log) > exposure(exp_f) scale(dev) irls; Iteration 1: deviance = Iteration 2: deviance = Iteration 3: deviance = Iteration 4: deviance = Iteration 5: deviance = Iteration 6: deviance = Iteration 7: deviance = Intrinsic estimator of APC effects No of obs = 152 Optimization : MQL Fisher scoring Residual df = 102 (IRLS EIM) Scale parameter = 1 Deviance = (1/df) Deviance = Pearson = (1/df) Pearson =
8 AIC = Deviance = BIC = EIM age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ period_ period_ period_ period_ period_ period_ period_ period_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ cohort_
9 cohort_ cohort_ cohort_ cohort_ cohort_ cohort_ _cons apc_cglim death_f if age<=90, > age(age) period(year) cohort(cohort) > agepfx("_a") periodpfx("_p") cohortpfx("_c") > family(poisson) link(log) > exposure(exp_f) scale(dev) irls constraint("a5=a10"); Iteration 1: deviance = Iteration 2: deviance = Iteration 3: deviance = Iteration 4: deviance = Iteration 5: deviance = Iteration 6: deviance = Iteration 7: deviance = Generalized linear models No of obs = 152 Optimization : MQL Fisher scoring Residual df = 102 (IRLS EIM) Scale parameter = 1 Deviance = (1/df) Deviance = Pearson = (1/df) Pearson = BIC = EIM _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_
10 _P_ _P_ _P_ _P_ _P_ _P_ _P_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _cons (Standard errors scaled using square root of deviance-based dispersion) _A_5=_A_10 drop _A* _P* _C*; apc_cglim death_f if age<=90, > age(age) period(year) cohort(cohort) > agepfx("_a") periodpfx("_p") cohortpfx("_c") > family(poisson) link(log) > exposure(exp_f) scale(dev) irls constraint("p1965=p1960"); Iteration 1: deviance = Iteration 2: deviance = Iteration 3: deviance = Iteration 4: deviance = Iteration 5: deviance = Iteration 6: deviance = Iteration 7: deviance = Generalized linear models No of obs = 152 Optimization : MQL Fisher scoring Residual df = 102
11 (IRLS EIM) Scale parameter = 1 Deviance = (1/df) Deviance = Pearson = (1/df) Pearson = BIC = EIM _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _P_ _P_ _P_ _P_ _P_ _P_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_
12 _C_ _C_ _C_ _C_ _C_ _cons (Standard errors scaled using square root of deviance-based dispersion) _P_1965=_P_1960 drop _A* _P* _C*; apc_cglim death_f if age<=90, > age(age) period(year) cohort(cohort) > agepfx("_a") periodpfx("_p") cohortpfx("_c") > family(poisson) link(log) > exposure(exp_f) scale(dev) irls constraint("c1995=c1990"); Iteration 1: deviance = Iteration 2: deviance = Iteration 3: deviance = Iteration 4: deviance = Iteration 5: deviance = Iteration 6: deviance = Iteration 7: deviance = Generalized linear models No of obs = 152 Optimization : MQL Fisher scoring Residual df = 102 (IRLS EIM) Scale parameter = 1 Deviance = (1/df) Deviance = Pearson = (1/df) Pearson = BIC = EIM _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_ _A_
13 _A_ _A_ _A_ _A_ _P_ _P_ _P_ _P_ _P_ _P_ _P_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _C_ _cons (Standard errors scaled using square root of deviance-based dispersion) _C_1995=_C_1990 drop _A* _P* _C*; #delim cr delimiter now cr log close log: /mnt/ide1/home/sschulh1/apc/apc_examplelog log type: text closed on: 21 Jul 2006, 18:08:28 -
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