Dear all
I'm working in Stata 14.2.
I'm doing a two-level logit model with melogit. I'm interested in a cross-level interaction between the variable win (first level) and the variable camp (second level).
I think in this case, I have to take the first level variable in the random part of the model. Is this right?
Because win ist a factor variable i need the R. notation.
In the Stata manuals the .R notation ist normally used with the _all option. But this option takes all cluster together. I don't understand why this should makes sense.
So my question is what ist the difference between the following models? and which one should I take?

Code1
. melogit entscheid i.win##i.wissen i.win##i.prä i.win##i.Wirtschaft age i.educ i.sex i.lang i.win##c.camp || _all: R.win, cov(unstructured) intpoints(30)

Code2 with group identity variable
melogit entscheid i.win##i.wissen i.win##i.prä i.win##i.Wirtschaft i.win##ib2.zukunft age i.educ i.sex i.lang i.win##c.camp || idnr:R.win, covariance(unstructured) intpoints(30)

idnr ist the identifying variable.

And why is there only one var(_cons) in the output? Should not be there a variance for each category of win?