You are not creating your data set properly.
Your 'mat' is:
> mat
column1 column2
1 1 0
2 1 0
3 0 1
4 0 0
5 1 1
6 1 0
7 1 0
8 0 1
9 0 0
10 1 1
What you really want is:
DF <- data.frame(y = c(1,0,1,0,0,1,0,0,1,1), x = c(5,4,1,6,3,6,5,3,7,9))
> DF
y x
1 1 5
2 0 4
3 1 1
4 0 6
5 0 3
6 1 6
7 0 5
8 0 3
9 1 7
10 1 9
MOD <- glm(y ~ x, data = DF, family = binomial)
> summary(MOD)
Call:
glm(formula = y ~ x, family = binomial, data = DF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.3353 -1.0229 -0.1239 0.9956 1.7477
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.6118 1.7833 -0.904 0.366
x 0.3293 0.3383 0.973 0.330
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 13.863 on 9 degrees of freedom
Residual deviance: 12.767 on 8 degrees of freedom
AIC: 16.767
Number of Fisher Scoring iterations: 4
HTH,
Marc Schwartz
On Nov 12, 2010, at 12:56 PM, Benjamin Godlove wrote:
> I think it is likely I am missing something. Here is a very simple example:
>
> R code:
>
> mat <- matrix(nrow = 10, ncol = 2, c(1,0,1,0,0,1,0,0,1,1),
> c(5,4,1,6,3,6,5,3,7,9), dimnames = list(c(1,2,3,4,5,6,7,8,9,10),
> c("column1","column2")))
>
> g <- glm(mat[1:10] ~ mat[11:20], family = binomial (link = logit))
>
> g$converged
>
>
> SAS code:
>
> data mat;
> input col1 col2;
> datalines;
> 1 5
> 0 4
> 1 1
> 0 6
> 0 3
> 1 6
> 0 5
> 0 3
> 1 7
> 1 9
> ;
>
> proc logistic data=mat descending;
> model col1 = col2 / link=logit;
> run;
>
> SAS output (in case you don't have access to SAS):
> Convergence criterion satisfied
>
> Estimate SE
> Intercept -1.6118 1.7833
> col2 0.3293 0.3383
>
>
> Of course, with an example this small, it is not so surprising that the two
> methods differ; and they hardly differ by a single S. But as the datasets
> get larger, the difference is more pronounced. Let me know if you would
> like me to send you a large dataset. I get the feeling I am doing something
> wrong in R, so please let me know what you think.
>
> Thank you!
>
> Ben Godlove
>
> On Thu, Nov 11, 2010 at 1:59 PM, Albyn Jones <jo...@reed.edu> wrote:
>
>> do you have factors (categorical variables) in the model? it could be
>> just a parameterization difference.
>>
>> albyn
>>
>> On Thu, Nov 11, 2010 at 12:41:03PM -0500, Benjamin Godlove wrote:
>>> Dear R developers,
>>>
>>> I have noticed a discrepancy between the coefficients returned by R's
>> glm()
>>> for logistic regression and SAS's PROC LOGISTIC. I am using dist =
>> binomial
>>> and link = logit for both R and SAS. I believe R uses IRLS whereas SAS
>> uses
>>> Fisher's scoring, but the difference is something like 100 SE on the
>>> intercept. What accounts for such a huge difference?
>>>
>>> Thank you for your time.
>>>
>>> Ben Godlove
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>> --
>> Albyn Jones
>> Reed College
>> jo...@reed.edu
>>
>>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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