[R] Genmod in SAS vs. glm in R

[Rolf Turner]

For one thing your call to glm() is wrong --- didn't you notice the
warning messages about ``non-integer #successes in a binomial glm!''?

You need to do either:

glm(r/k ~ x, family=binomial(link='cloglog'), data=bin_data,  
offset=log(y), weights=k)

or:

glm(cbind(r,k-r) ~ x, family=binomial(link='cloglog'), data=bin_data,  
offset=log(y))

You get the same answer with either, but this answer still does not  
agree with your
SAS results.  Perhaps you have an error in your SAS syntax as well.   
I wouldn't know.

	cheers,

		Rolf Turner

	
On 10/09/2008, at 10:37 AM, sandsky wrote:

>
> 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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