multilevel, multinomial logistic regression error message with xtmlogit but not with gsem

[Suvi Maatta]

Dear all,

I am interested in conducting a multilevel, multinomial logistic regression model in Stata. The dependent variable is a categorical with 4 distinct groups. The explanatory factors include factors of individual background and characteristics of school and neighborhood settings (as a group average). The data is nested with schools (pupils in schools).

I conducted the analyses with gsem at first, and it worked fine. Then, I found this discussion (https://www.statalist.org/forums/for...random-effects ), and thus, tried to conduct the analysis by using xtmlogit with school random effects. I did get following error message: Cannot compute an improvement -- discontinuous region encountered. I tested it so that I included one predictor at a time without any luck. Now, I wonder what could explain this error message?
Code: xtmlogit expclass i.familyeconomic3 if otos==1, re rrr vce(cluster koulu_koodi)

As I was able to conduct the analyses by using gsem, I wonder if I could still report the results based on gsem?

Thank you for all the help,
Suvi

[Richard Williams]

What was the gsem command? We want to make sure the 2 commands really were equivalent. Also what was your xtset command?

Some generic advice on what to do when you have convergence problems can be found at

https://www3.nd.edu/~rwilliam/xsoc73994/L02.pdf

[Suvi Maatta]

Hi, thank you for the reply.
Gsem command: gsem (i.expclass <- i.familyeconomic3 M[school_code]), mlogit

...and just noticed that I did not translate the measures in English in my previous post, so xtset-command was "xtset koulu_koodi" (in english: "xtset school_code").

Code: xtmlogit expclass i.familyeconomic3 if sample==1, re rrr vce(cluster school_code)

Suvi

[Richard Williams]

Thanks. In the xtmlogit command you are restricting it to cases where sample equals 1, but in the gsem command there is no such restriction.

If I am reading it right, the two commands are not equivalent. Try either limiting the sample the same way in gsem or drop the sample restriction in xtmlogit.

You also have the vce option in xtmlogit but not in gsem, so perhaps that affects things.