Hello, I have different results from these two softwares for a simple binomial GLM problem. >From Genmod in SAS: LogLikelihood=-4.75, coeff(intercept)=-3.59, coeff(x)=0.95 >From glm in R: LogLikelihood=-0.94, coeff(intercept)=-3.99, coeff(x)=1.36
Is there anyone tell me what I did wrong? Here are the code and results, 1) SAS Genmod: % r: # of failure % k: size of a risk set data bin_data; input r k y x; os=log(y); cards; 1 3 5 0.5 0 2 5 0.5 0 2 4 1.0 1 2 4 1.0 ; proc genmod data=nelson; model r/k = x / dist = binomial link =cloglog offset = os ; <Results from SAS> Log Likelihood -4.7514 Parameter DF Estimate Error Limits Square Pr > ChiSq Intercept 1 -3.6652 1.9875 -7.5605 0.2302 3.40 0.0652 x 1 0.8926 2.4900 -3.9877 5.7728 0.13 0.7200 Scale 0 1.0000 0.0000 1.0000 1.0000 2) glm in R bin_data <- data.frame(cbind(y=c(5,5,4,4),r=c(1,0,0,1),k=c(3,2,2,2),x=c(0.5,0.5,1.0,1.0))) glm(r/k ~ x, family=binomial(link='cloglog'), data=bin_data, offset=log(y)) <Results from R> Coefficients: (Intercept) x -3.991 1.358 'log Lik.' -0.9400073 (df=2) -- View this message in context: http://www.nabble.com/Genmod-in-SAS-vs.-glm-in-R-tp19402751p19402751.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.