Ioannis,
Here's an illustrative example. Note that: glm also objects to X4; X1,..,X4
are defined as factors.
I've looked (albeit in a crude way) at various examples using the perturb
package and it seems to confirm that X4 is the source of multicollinearity.
As I say, I think the constant row-su
I'm running brglm to do binomial loguistic regression.
The perhaps multicollinearity-related feature(s) are:
(1) the k IVs are all binary categorical, coded as 0 or 1;
(2) each row of the IVs contains exactly C (< k) 1's; (I think this is the
source of the problem)
(3) there are n * k unique r
I'm running brglm with binomial loguistic regression. The perhaps
multicollinearity-related feature(s) are:
(1) the k IVs are all binary categorical, coded as 0 or 1;
(2) each row of the IVs contains exactly C (< k) 1's;
(3) k IVs, there are n * k unique rows;
(4) when brglm is run, at least
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