4.6 Fitting generalized linear models

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Presentation transcript:

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

4.6.1 Newton-Raphson method

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;

4.6.2 Fisher scoring method

4.7 Quasi-likelihood and generalized linear models

4.7.2 Overdispersion for Poisson GLMs and quasi-likelihood

4.7.4 Teratology overdispersion example

SAS code data moore; input litter group n y @@; datalines; 1 1 10 1 2 1 11 4 3 1 12 9 4 1 4 4 5 1 10 10 … 55 4 14 1 56 4 8 0 58 4 17 0 ; proc genmod; class group; model y/n = group / dist = bin link = identity noint; estimate ‘pi1- pi2 ’ group 1 -1 0 0; model y/n = group / dist = bin link = identity noint scale = pearson;