Mplus DEVELOPMENT (Mpdev 9/4/2025, Build 2)
MUTHEN & MUTHEN
09/05/2025  10:54 AM

INPUT INSTRUCTIONS

  Title:
      Last BCH step

  data:
      file =  bch2LCA.dat;

  variable:
      NAMES = u1-u12 w1-w9;
      usev = w1-w9;
      missing = *;

      classes  = c1(3) c2(3);



      training = w1-w9(bch);


  analysis:
      type = mixture;

      starts = 0;

  Model:
      %overall%
      c2 on c1;




INPUT READING TERMINATED NORMALLY




Last BCH step

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        2000

Number of dependent variables                                    0
Number of independent variables                                  0
Number of continuous latent variables                            0
Number of categorical latent variables                           2

Observed dependent variables

Categorical latent variables
   C1          C2

Variables with special functions

  Training variables (BCH)
   W1          W2          W3          W4          W5          W6
   W7          W8          W9


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-06
    Relative loglikelihood change                        0.100D-06
    Derivative                                           0.100D-05
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-05
  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-05
  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

Input data file(s)
  bch2LCA.dat
Input data format  FREE


SUMMARY OF DATA



COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        8

Loglikelihood

          H0 Value                       -4164.690
          H0 Scaling Correction Factor      2.4648
            for MLR

Information Criteria

          Akaike (AIC)                    8345.380
          Bayesian (BIC)                  8390.187
          Sample-Size Adjusted BIC        8364.771
            (n* = (n + 2) / 24)



FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE
BASED ON THE ESTIMATED MODEL

  Latent Class
    Variable    Class

    C1             1       654.00568          0.32700
                   2       830.55475          0.41528
                   3       515.43958          0.25772
    C2             1       458.22568          0.22911
                   2       610.90778          0.30545
                   3       930.86658          0.46543


FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON THE ESTIMATED MODEL

  Latent Class
    Pattern

    1  1        196.56434          0.09828
    1  2        186.23587          0.09312
    1  3        271.20549          0.13560
    2  1        172.11949          0.08606
    2  2        152.12137          0.07606
    2  3        506.31388          0.25316
    3  1         89.54183          0.04477
    3  2        272.55054          0.13628
    3  3        153.34718          0.07667

     Additional output is available using the TECH15 option of the OUTPUT command.


LATENT TRANSITION PROBABILITIES BASED ON THE ESTIMATED MODEL

  C1 Classes (Rows) by C2 Classes (Columns)

            1        2        3

   1     0.301    0.285    0.415
   2     0.207    0.183    0.610
   3     0.174    0.529    0.298


TRANSITION PROBABILITY ODDS

     TRANSITION TABLE ODDS AND 95% CONFIDENCE INTERVALS FOR C1 TO C2
        1.000(1.000,1.000)   0.947(0.668,1.344)   1.380(1.007,1.890)
        1.131(0.783,1.635)   1.000(1.000,1.000)   3.328(2.555,4.336)
        0.584(0.364,0.937)   1.777(1.327,2.381)   1.000(1.000,1.000)


C-SPECIFIC CLASSIFICATION RESULTS

Classification Quality for C1

     Entropy is not available with BCH weights.

BCH Weights for the Most Likely Latent Class Membership (Row)
by Latent Class (Column)

           1        2        3

    1   1.285    -.131    -.154
    2   -.138    1.141    -.003
    3   -.112    -.063    1.176


Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)

           1        2        3

    1   1.266    -.176    -.090
    2   -.102    1.142    -.040
    3   -.193    -.005    1.197


Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)

              1        2        3

    1     14.051    0.000    0.000
    2      0.000   13.948    0.000
    3    -13.996  -13.996    0.000

Classification Quality for C2

     Entropy is not available with BCH weights.

BCH Weights for the Most Likely Latent Class Membership (Row)
by Latent Class (Column)

           1        2        3

    1   1.211    -.108    -.103
    2   -.165    1.253    -.088
    3   -.133    -.012    1.146


Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)

           1        2        3

    1   1.459    -.195    -.263
    2   -.098    1.116    -.018
    3   -.061    -.052    1.113


Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)

              1        2        3

    1     14.193    0.000    0.000
    2      0.000   13.925    0.000
    3    -13.922  -13.922    0.000


MODEL RESULTS

                                                    Two-Tailed
                    Estimate       S.E.  Est./S.E.    P-Value

Categorical Latent Variables

 C2#1     ON
    C1#1               0.216      0.319      0.678      0.498
    C1#2              -0.541      0.284     -1.904      0.057

 C2#2     ON
    C1#1              -0.951      0.233     -4.075      0.000
    C1#2              -1.778      0.205     -8.680      0.000

 Means
    C1#1               0.238      0.081      2.954      0.003
    C1#2               0.477      0.067      7.103      0.000
    C2#1              -0.538      0.241     -2.230      0.026
    C2#2               0.575      0.149      3.854      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.189E-01
       (ratio of smallest to largest eigenvalue)


DIAGRAM INFORMATION

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


     Beginning Time:  10:54:56
        Ending Time:  10:54:56
       Elapsed Time:  00:00:00



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