Hi statalist,

I have run a latent class model with a discrete choice experiment data (9882 observations) using the lclogit package written by Pacifico and Yoo (http://www.stata-journal.com/article.html?artic).

I would like to ask two questions:
  1. Why Stata shows me the model fit indicators of 2-7 classes multiple times? (I have a few guesses but I'd like to confirm)
Should I choose the 4-classes model? (see code below) Besides, if I remove all the membership variables, the best model is the 3-classes model.
  1. I have another model which can't converge if the number of classes is more than 5. In this case, should I use a 4-classes model if it is the best fit model?
BTW, I have also tried the lclogit2 and I have specified all attributes as random variables. But the doesn't converge if the number of classes is more than 3. So, I prefer to stay with lclogit.

Any thoughts and suggestions are greatly appreciated.

Many thanks!

Best,
Gengyang

Code:
. forvalues c = 2/7 {
  2.                 quietly lclogit choice price attribute2 attribute3 attribute4 attribute5 attribute6, group(group) id(id) nclasses(`c') membe
> rship(x1 x2 x3 x4 x5 x6)
  3.                 matrix b = e(b)
  4.                 matrix ic = nullmat(ic) \ `e(nclasses)', `e(ll)',`=colsof(b)', `e(aic)', `e(caic)', `e(bic)'
  5.          }
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular
Warning:  variance matrix is nonsymmetric or highly singular

.
. matrix colnames ic = "Classess" "LLF" "Nparam" "AIC" "CAIC" "BIC"

.
. matlist ic, name(columns)

 Classess        LLF     Nparam        AIC       CAIC        BIC
-----------------------------------------------------------------
        2  -2856.544         19   5751.088   5851.941   5832.941
        3   -2809.69         31   5681.381   5845.932   5814.932
        4   -2734.25         43   5554.501   5782.749   5739.749
        5  -2703.635         55   5517.269   5809.215   5754.215
        2  -2856.544         19   5751.088   5851.941   5832.941
        3  -2809.691         31   5681.381   5845.932   5814.932
        4   -2758.73         43   5603.461   5831.709   5788.709
        5  -2705.848         55   5521.696   5813.642   5758.642
        6  -2681.539         67   5497.077    5852.72    5785.72
        7  -2657.916         79   5473.832   5893.172   5814.172
        2  -2856.544         19   5751.088   5851.941   5832.941
        3  -2809.691         31   5681.381   5845.932   5814.932
        4   -2758.73         43   5603.461   5831.709   5788.709
        5  -2705.848         55   5521.696   5813.642   5758.642
        6  -2681.539         67   5497.077    5852.72    5785.72
        7  -2657.914         79   5473.828   5893.168   5814.168
        2  -2848.066         20   5736.131   5842.293   5822.293
        3  -2799.096         33   5664.193    5839.36    5806.36
        4  -2746.507         46   5585.015   5829.187   5783.187
        5  -2692.474         59   5502.948   5816.126   5757.126
        6  -2670.848         72   5485.697    5867.88    5795.88
        7  -2641.101         85   5452.203   5903.391   5818.391
        2  -2846.607         24   5741.214   5868.608   5844.608
        3  -2796.342         41   5674.683   5892.316   5851.316
        4  -2740.002         58   5596.004   5903.874   5845.874
        5  -2684.407         75   5518.814   5916.922   5841.922
        6      -2659         92       5502   5990.346   5898.346
        7  -2627.486        109   5472.972   6051.555   5942.555
.