On 12.11.2010 20:11, Marc Schwartz wrote:
You are not creating your data set properly.
Your 'mat' is:
mat
column1 column2
11 0
21 0
30 1
40 0
51 1
61 0
71 0
80 1
90
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))
You are not creating your data set properly.
Your 'mat' is:
mat
column1 column2
11 0
21 0
30 1
40 0
51 1
61 0
71 0
80 1
90 0
10 1 1
What you really want
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
Is the algorithm converging? Is there separation (i.e.,
perfect predictor) in the model?
Are you getting a warning about fitted probabilities of
0 or 1?, etc.
We would need much more information (preferably a reproducible
example) before we can help.
Benjamin Godlove wrote:
Dear R developers,
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
Albyn Jones jones at reed.edu writes:
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
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