* Gamma Chapter drop _all set seed0 23988 /* Set seed for genbinomial pgm */ set seed 238947 /* Set seed for reproducibility */ set obs 10000 /* Number of obs = 10,000 */ gen x1 = invnormal(uniform()) /* Creation of random variates */ gen x2 = invnormal(uniform()) gen x3 = invnormal(uniform()) gen d = 50+5*int((_n-1)/1000) /* Creation of 10 different denominators */ tab d /* Tabulation of the denominator */ set seed 0 generate xb = 1 + .5*x1 - .75*x2 + .25*x3 /* Create linear predictor */ genbinomial y, xbeta(xb) de(d) /* Create simulated dataset */ glm y x1 x2 x3, family(bin d) nolog /* Model y on x1, x2, and x3 */ glm y x2 x3, family(bin d) nolog /* Here, we omit the x1 covariate */ gen x23 = x2*x3 /* create interaction */ gen xb1 = 1 + .5*x1 - .75*x2 + .25*x3 + .2*x23 /* create linear predictor */ genbinomial y1, xbeta(xb1) de(d) /* create simulated dataset */ glm y1 x1 x2 x3 x23, family(bin d) nolog glm y1 x1 x2 x3, family(bin d) nolog use heart01, clear glm death anterior hcabg kk2 kk3 kk4 age2-age4, family(bin) irls scale(dev) eform nolog noheader egen grp=group(anterior-age4) /* The covariate pattern is */ /* assigned a group number */ drop if grp == . /* Discard missing values */ egen cases = count(grp), by(grp) /* Size of covariate pattern */ egen die = total(death), by(grp) /* Deaths in pattern */ sort grp /* Sort by covariate pattern */ qui by grp: keep if _n==1 /* Keep one case per pattern */ glm die anterior hcabg kk2 kk3 kk4 age2-age4, family(bin cases) irls eform nolog glm die anterior hcabg kk2 kk3 kk4 age2-age4, family(bin cases) irls scale(dev) eform nolog noheader glm die anterior hcabg kk2 kk3 kk4 age2-age4, family(bin cases) l(loglog) scale(dev) eform irls nolog display chiprob(1, 3.14) gen t = _n tis t glm die anterior hcabg kk2 kk3 kk4 age2-age4, family(bin cases) l(loglog) irls nwest(anderson 2) nolog williams die cases anterior hcabg kk2 kk3 kk4 age2-age4, eform nolog