Hi Statalist!

I am trying to perform a latent class analysis and I am very new to this method, so I hope you will understand if my question is a bit naïve.
I would like to perform the analysis using nine categorical variables (each with five categories) and obtain three classes. Here is the code I am using:

Code:
gsem (e4_1b e4_2b e4_3b e4_4b e4_5b e4_6b e4_7b e4_8b e4_9b <-, ologit), lclass(C 3)
Once I try to estimate the marginal predicted means of the outcome within each latent class (estat lcmean), the command seems to run forever, without giving any output. Looking on the forum, I am supposing that the issue is related to one coefficient being above 15. Below you can find part of the output, and the high coefficient in red.

Code:
Class          : 2

Response       : e4_1b
Family         : ordinal
Link           : logit

Response       : e4_2b
Family         : ordinal
Link           : logit

Response       : e4_3b
Family         : ordinal
Link           : logit

Response       : e4_4b
Family         : ordinal
Link           : logit

Response       : e4_5b
Family         : ordinal
Link           : logit

Response       : e4_6b
Family         : ordinal
Link           : logit

Response       : e4_7b
Family         : ordinal
Link           : logit

Response       : e4_8b
Family         : ordinal
Link           : logit

Response       : e4_9b
Family         : ordinal
Link           : logit

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
/e4_1b       |
        cut1 |  -6.207387   .2099544                      -6.61889   -5.795884
        cut2 |  -4.585708     .09238                     -4.766769   -4.404646
        cut3 |  -1.894731   .0285634                     -1.950714   -1.838748
        cut4 |   .7498389   .0206396                      .7093861    .7902917
-------------+----------------------------------------------------------------
/e4_2b       |
        cut1 |  -3.075159   .0378004                     -3.149246   -3.001071
        cut2 |  -.6201423   .0158303                     -.6511692   -.5891154
        cut3 |   1.625804   .0206937                      1.585246    1.666363
        cut4 |   4.059866   .0600725                      3.942126    4.177606
-------------+----------------------------------------------------------------
/e4_3b       |
        cut1 |  -4.235327   .0753749                     -4.383059   -4.087595
        cut2 |  -2.243794   .0294187                     -2.301453   -2.186134
        cut3 |   -.249513   .0220957                     -.2928197   -.2062063
        cut4 |   2.190033   .0348925                      2.121645    2.258421
-------------+----------------------------------------------------------------
/e4_4b       |
        cut1 |  -24.39119   2397.723                     -4723.841    4675.059
        cut2 |  -3.252751   .0575151                     -3.365479   -3.140024
        cut3 |  -.6396046   .0260331                     -.6906285   -.5885807
        cut4 |    2.04866   .0369879                      1.976165    2.121155
-------------+----------------------------------------------------------------
/e4_5b       |
        cut1 |  -5.785578   .1817201                     -6.141743   -5.429414
        cut2 |   -2.87042   .0414458                     -2.951652   -2.789188
        cut3 |  -.8560699   .0229513                     -.9010535   -.8110862
        cut4 |   1.127883   .0237217                       1.08139    1.174377
-------------+----------------------------------------------------------------
/e4_6b       |
        cut1 |  -.2159903   .0155038                     -.2463771   -.1856035
        cut2 |   1.242691   .0190969                      1.205261     1.28012
        cut3 |   2.667265   .0303306                      2.607818    2.726711
        cut4 |   5.438793   .1438952                      5.156764    5.720823
-------------+----------------------------------------------------------------
/e4_7b       |
        cut1 |   -1.62553   .0205138                     -1.665736   -1.585324
        cut2 |   .0296444   .0151189                      .0000119    .0592768
        cut3 |   1.490041   .0191115                      1.452583    1.527499
        cut4 |   3.487356   .0482501                      3.392788    3.581925
-------------+----------------------------------------------------------------
/e4_8b       |
        cut1 |   1.331648    .023303                      1.285975    1.377321
        cut2 |   2.582377   .0354519                      2.512892    2.651861
        cut3 |    3.56694    .043742                      3.481207    3.652673
        cut4 |   6.136812   .1994962                      5.745807    6.527817
-------------+----------------------------------------------------------------
/e4_9b       |
        cut1 |   .0988198   .0149272                      .0695631    .1280765
        cut2 |   1.434078   .0194443                      1.395968    1.472188
        cut3 |   2.952968   .0329211                      2.888444    3.017492
        cut4 |   5.747459   .1649939                      5.424077    6.070841
------------------------------------------------------------------------------
As I understood, I should constrain the coefficient to 15. I tried to use the following code, but it is not adequate for my categorical variable.

Code:
gsem (e4_1b e4_2b e4_3b e4_4b e4_5b e4_6b e4_7b e4_8b e4_9b <- ) (2: e4_4b  <- _cons@15) , ologit lclass(C 3)
Could anyone please suggest me the right specifications? Any suggestion is be more than welcome,

Kind regards,
Marla