[R] Consistency of Logistic Regression

[Marc Schwartz]

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 <jones at 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 at 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
>> jones at reed.edu
>> 
>> 
> 
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help at 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.