Rolf Turner wrote:
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.
The data created in the data step are not those used in the analysis.
Changing to
data nelson;
<etc>
gives the same result as R on the versions I have available:
Analysis Of Parameter Estimates
Standard Wald 95%
Confidence Chi-
Parameter DF Estimate Error Limits
Square Pr > ChiSq
Intercept 1 -3.5866 2.2413 -7.9795
0.8064 2.56 0.1096
x 1 0.9544 2.8362 -4.6046
6.5133 0.11 0.7365
Scale 0 1.0000 0.0000 1.0000 1.0000
and
Call:
glm(formula = r/k ~ x, family = binomial(link = "cloglog"), data = bin_data,
weights = k, offset = log(y))
Deviance Residuals:
1 2 3 4
0.5407 -0.9448 -1.0727 0.7585
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.5866 2.2413 -1.600 0.110
x 0.9544 2.8362 0.336 0.736
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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