Mplus DEVELOPMENT (Mpdev 9/4/2025, Build 2)
MUTHEN & MUTHEN
09/05/2025  10:25 AM

INPUT INSTRUCTIONS

  Title:
      2-group regular LTA
      with x1 as group variable

  data:
      file = mgbch.dat;

  variable:
      NAMES = u1-u12 x1 y1-y3 w1-w18;
      usev = y1-y3;
      missing = *;

      classes  = c(2) c1(3) c2(3);

      categorical = y1-y2;

      training = w1-w18(bch);


  analysis:
      type = mixture;

      starts = 0;

  model:

     %overall%

     c2 on c1;
     c2 c1 on c;







*** WARNING in MODEL command
  All variables are uncorrelated with all other variables within class.
  Check that this is what is intended.
   1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS




2-group regular LTA
with x1 as group variable

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        2000

Number of dependent variables                                    3
Number of independent variables                                  0
Number of continuous latent variables                            0
Number of categorical latent variables                           3

Observed dependent variables

  Continuous
   Y3

  Binary and ordered categorical (ordinal)
   Y1          Y2

Categorical latent variables
   C           C1          C2

Variables with special functions

  Training variables (BCH)
   W1          W2          W3          W4          W5          W6
   W7          W8          W9          W10         W11         W12
   W13         W14         W15         W16         W17         W18


Estimator                                                      MLR
Information matrix                                        OBSERVED
Optimization Specifications for the Quasi-Newton Algorithm for
Continuous Outcomes
  Maximum number of iterations                                 100
  Convergence criterion                                  0.100D-05
Optimization Specifications for the EM Algorithm
  Maximum number of iterations                                 500
  Convergence criteria
    Loglikelihood change                                 0.100D-03
    Relative loglikelihood change                        0.100D-05
    Derivative                                           0.100D-03
Optimization Specifications for the M step of the EM Algorithm for
Categorical Latent Variables
  Number of M step iterations                                    1
  M step convergence criterion                           0.100D-03
  Basis for M step termination                           ITERATION
Optimization Specifications for the M step of the EM Algorithm for
Censored, Binary or Ordered Categorical (Ordinal), Unordered
Categorical (Nominal) and Count Outcomes
  Number of M step iterations                                    1
  M step convergence criterion                           0.100D-03
  Basis for M step termination                           ITERATION
  Maximum value for logit thresholds                            15
  Minimum value for logit thresholds                           -15
  Minimum expected cell size for chi-square              0.100D-01
Maximum number of iterations for H1                           2000
Convergence criterion for H1                             0.100D-03
Optimization algorithm                                         EMA
Parameterization                                             LOGIT
Link                                                         LOGIT

Input data file(s)
  mgbch.dat
Input data format  FREE


SUMMARY OF DATA

     Number of missing data patterns             1
     Number of y missing data patterns           1
     Number of u missing data patterns           1


COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100


     PROPORTION OF DATA PRESENT FOR Y


           Covariance Coverage
              Y3
              ________
 Y3             1.000


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    Y1
      Category 1    0.487          975.000
      Category 2    0.512         1025.000
    Y2
      Category 1    0.460          920.000
      Category 2    0.044           89.000
      Category 3    0.495          991.000



UNIVARIATE SAMPLE STATISTICS


     UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS

         Variable/         Mean/     Skewness/   Minimum/ % with                Percentiles
        Sample Size      Variance    Kurtosis    Maximum  Min/Max      20%/60%    40%/80%    Median

     Y3                    0.447       0.006      -5.754    0.05%      -1.089     -0.012      0.461
            2000.000       3.434      -0.144       6.409    0.05%       0.938      2.045


     WARNING: IN CLASS PATTERN 1 1 3 VARIABLE Y2 HAS A NEGATIVE FREQUENCY FOR CATEGORY 2.


     WARNING: IN CLASS PATTERN 1 3 1 VARIABLE Y2 HAS A NEGATIVE FREQUENCY FOR CATEGORY 2.


     WARNING: IN CLASS PATTERN 2 3 2 VARIABLE Y2 HAS A NEGATIVE FREQUENCY FOR CATEGORY 2.



     THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ERROR IN THE
     COMPUTATION.  CHANGE YOUR MODEL AND/OR STARTING VALUES.






MODEL RESULTS

                    Estimate

Parameters in the Overall Part of the Model (Parameters Equal in All of the Classes)

 Variances
    Y3                 2.026

Parameters for Class-specific Model Parts

Latent Class Pattern 1 1 1

 Means
    Y3                 0.152

 Thresholds
    Y1$1               1.081
    Y2$1               0.992
    Y2$2               1.079

Latent Class Pattern 1 1 2

 Means
    Y3                -0.729

 Thresholds
    Y1$1               0.827
    Y2$1              -0.139
    Y2$2               0.492

Latent Class Pattern 1 1 3

 Means
    Y3                -1.687

 Thresholds
    Y1$1               0.392
    Y2$1               1.179
    Y2$2               1.082

Latent Class Pattern 1 2 1

 Means
    Y3                -0.118

 Thresholds
    Y1$1               1.096
    Y2$1               0.904
    Y2$2               1.073

Latent Class Pattern 1 2 2

 Means
    Y3                -0.458

 Thresholds
    Y1$1               0.747
    Y2$1              -0.244
    Y2$2               0.013

Latent Class Pattern 1 2 3

 Means
    Y3                -1.114

 Thresholds
    Y1$1              -0.882
    Y2$1              -0.024
    Y2$2               0.229

Latent Class Pattern 1 3 1

 Means
    Y3                 0.970

 Thresholds
    Y1$1               1.643
    Y2$1               1.296
    Y2$2               1.242

Latent Class Pattern 1 3 2

 Means
    Y3                -0.252

 Thresholds
    Y1$1               0.877
    Y2$1               0.202
    Y2$2               0.932

Latent Class Pattern 1 3 3

 Means
    Y3                -1.086

 Thresholds
    Y1$1              -0.297
    Y2$1              -0.001
    Y2$2               0.129

Latent Class Pattern 2 1 1

 Means
    Y3                 1.866

 Thresholds
    Y1$1              -0.329
    Y2$1              -0.176
    Y2$2              -0.018

Latent Class Pattern 2 1 2

 Means
    Y3                 0.631

 Thresholds
    Y1$1               0.375
    Y2$1              -0.779
    Y2$2              -0.612

Latent Class Pattern 2 1 3

 Means
    Y3                -0.228

 Thresholds
    Y1$1              -1.758
    Y2$1              -1.137
    Y2$2              -0.721

Latent Class Pattern 2 2 1

 Means
    Y3                 2.000

 Thresholds
    Y1$1              -0.162
    Y2$1              -0.685
    Y2$2              -0.462

Latent Class Pattern 2 2 2

 Means
    Y3                 0.899

 Thresholds
    Y1$1              -0.474
    Y2$1              -0.867
    Y2$2              -0.788

Latent Class Pattern 2 2 3

 Means
    Y3                 1.048

 Thresholds
    Y1$1              -1.776
    Y2$1              -2.436
    Y2$2              -1.713

Latent Class Pattern 2 3 1

 Means
    Y3                 2.034

 Thresholds
    Y1$1              -0.133
    Y2$1              -0.597
    Y2$2              -0.491

Latent Class Pattern 2 3 2

 Means
    Y3                 1.446

 Thresholds
    Y1$1             -15.000
    Y2$1              -2.212
    Y2$2              -2.355

Latent Class Pattern 2 3 3

 Means
    Y3                 0.698

 Thresholds
    Y1$1              -2.316
    Y2$1              -1.448
    Y2$2              -1.368

Categorical Latent Variables

 C2#1     ON
    C1#1               1.628
    C1#2               0.943
    C#1               -1.204

 C2#2     ON
    C1#1               0.950
    C1#2               1.015
    C#1               -0.547

 C1#1     ON
    C#1               -0.766

 C1#2     ON
    C#1               -0.314

 Means
    C#1               -0.044
    C1#1               0.815
    C1#2               0.216
    C2#1               0.126
    C2#2              -0.501


MODEL COMMAND WITH FINAL ESTIMATES USED AS STARTING VALUES

     %OVERALL%

     c2#1 ON c1#1*1.62821;
     c2#1 ON c1#2*0.94301;
     c2#1 ON c#1*-1.20362;
     c2#2 ON c1#1*0.95023;
     c2#2 ON c1#2*1.01469;
     c2#2 ON c#1*-0.54656;
     c1#1 ON c#1*-0.76589;
     c1#2 ON c#1*-0.31373;

     [ c#1*-0.04351 ];
     [ c1#1*0.81488 ];
     [ c1#2*0.21614 ];
     [ c2#1*0.12582 ];
     [ c2#2*-0.50116 ];

     y3*2.02633 (2);

     %C#1.C1#1.C2#1%

     [ y3*0.15202 ];

     [ y1$1*1.08118 ];
     [ y2$1*0.99154 ];
     [ y2$2*1.07890 ];

     %C#1.C1#1.C2#2%

     [ y3*-0.72864 ];

     [ y1$1*0.82658 ];
     [ y2$1*-0.13908 ];
     [ y2$2*0.49226 ];

     %C#1.C1#1.C2#3%

     [ y3*-1.68713 ];

     [ y1$1*0.39173 ];
     [ y2$1*1.17947 ];
     [ y2$2*1.08195 ];

     %C#1.C1#2.C2#1%

     [ y3*-0.11814 ];

     [ y1$1*1.09649 ];
     [ y2$1*0.90399 ];
     [ y2$2*1.07268 ];

     %C#1.C1#2.C2#2%

     [ y3*-0.45750 ];

     [ y1$1*0.74747 ];
     [ y2$1*-0.24448 ];
     [ y2$2*0.01260 ];

     %C#1.C1#2.C2#3%

     [ y3*-1.11357 ];

     [ y1$1*-0.88245 ];
     [ y2$1*-0.02444 ];
     [ y2$2*0.22933 ];

     %C#1.C1#3.C2#1%

     [ y3*0.96973 ];

     [ y1$1*1.64343 ];
     [ y2$1*1.29583 ];
     [ y2$2*1.24176 ];

     %C#1.C1#3.C2#2%

     [ y3*-0.25175 ];

     [ y1$1*0.87661 ];
     [ y2$1*0.20222 ];
     [ y2$2*0.93180 ];

     %C#1.C1#3.C2#3%

     [ y3*-1.08562 ];

     [ y1$1*-0.29738 ];
     [ y2$1*-0.00057 ];
     [ y2$2*0.12871 ];

     %C#2.C1#1.C2#1%

     [ y3*1.86580 ];

     [ y1$1*-0.32863 ];
     [ y2$1*-0.17592 ];
     [ y2$2*-0.01825 ];

     %C#2.C1#1.C2#2%

     [ y3*0.63104 ];

     [ y1$1*0.37525 ];
     [ y2$1*-0.77887 ];
     [ y2$2*-0.61178 ];

     %C#2.C1#1.C2#3%

     [ y3*-0.22764 ];

     [ y1$1*-1.75795 ];
     [ y2$1*-1.13707 ];
     [ y2$2*-0.72071 ];

     %C#2.C1#2.C2#1%

     [ y3*1.99978 ];

     [ y1$1*-0.16172 ];
     [ y2$1*-0.68525 ];
     [ y2$2*-0.46208 ];

     %C#2.C1#2.C2#2%

     [ y3*0.89857 ];

     [ y1$1*-0.47353 ];
     [ y2$1*-0.86730 ];
     [ y2$2*-0.78789 ];

     %C#2.C1#2.C2#3%

     [ y3*1.04830 ];

     [ y1$1*-1.77552 ];
     [ y2$1*-2.43579 ];
     [ y2$2*-1.71265 ];

     %C#2.C1#3.C2#1%

     [ y3*2.03367 ];

     [ y1$1*-0.13277 ];
     [ y2$1*-0.59724 ];
     [ y2$2*-0.49135 ];

     %C#2.C1#3.C2#2%

     [ y3*1.44630 ];

     [ y1$1*-15 ];
     [ y2$1*-2.21194 ];
     [ y2$2*-2.35465 ];

     %C#2.C1#3.C2#3%

     [ y3*0.69811 ];

     [ y1$1*-2.31628 ];
     [ y2$1*-1.44829 ];
     [ y2$2*-1.36834 ];



DIAGRAM INFORMATION

  Mplus diagrams are currently not available for Mixture analysis.
  No diagram output was produced.


     Beginning Time:  10:25:13
        Ending Time:  10:25:13
       Elapsed Time:  00:00:00



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