# [R] Genmod in SAS vs. glm in R

sandsky realstone at hotmail.com
Wed Sep 10 00:37:28 CEST 2008

```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)
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