R help - Sep 2008 - Genmod in SAS vs. glm in R

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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Sent from the R help mailing list archive at Nabble.com.

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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