Genmod in SAS vs. glm in R

Whats the R equivalent for Proc logistic in SAS ? 

Is there a stepwise
method there ?

How to create scoring models in R , for larger datasets (200 mb), Is
there a way to compress and use datasets (like options compress=yes;)

On Wed, Sep 10, 2008 at 11:12 AM, Peter Dalgaard
<p.dalgaard at biostat.ku.dk> wrote:

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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