1. 4.6 Fitting generalized linear models
Newton-Raphson method
Fisher scoring method

2. 4.6.1 Newton-Raphson method

8. SAS code
/*Newton-Raphson*/ proc IML; y=20; n=100; u=1:10; H=1:10; b=1:10; b[1]=0.1; do i=1 to 10; H[i+1]=-y/b[i]**2-(n-y)/(1-b[i])**2; u[i+1]=(y-n*b[i])/(b[i]*(1-b[i])); b[i+1]=b[i]-1/H[i+1] *u[i+1]; end; print b;

9. 4.6.2 Fisher scoring method

13. 4.7 Quasi-likelihood and generalized linear models

16. 4.7.2 Overdispersion for Poisson GLMs and quasi-likelihood

27. 4.7.4 Teratology overdispersion example

31. SAS code data moore; input litter group n y @@; datalines; … ;
proc genmod; class group;
model y/n = group / dist = bin link = identity noint;
estimate ‘pi1- pi2 ’ group ;
model y/n = group / dist = bin link = identity noint scale = pearson;