Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010  11:18 PM

INPUT INSTRUCTIONS

  TITLE:                growth4.inp  normal, covariate, no missing
  MONTECARLO:                NAMES ARE y1-y4 x;
                          CUTPOINTS = x (0);
                          NOBSERVATIONS = 600;
                          NREPS = 10000;
                          SEED = 53487;
                          CLASSES = C(1);
                          GENCLASSES = C(1);
                          SAVE = growth4.sav;
  ANALYSIS:                 TYPE = MIXTURE;
                          ESTIMATOR = ML;
  MODEL MONTECARLO:
                          %OVERALL%
                          [x@0]; x@1;
                          i BY y1-y4@1;
                          s BY y1@0 y2@1 y3@2 y4@3;
                          [y1-y4@0];
                          [i*0 s*.2];
                          i*.25;
                          s*.09;
                          i WITH s*0;
                          y1-y4*.5;

                          i ON x*.5;
                          s ON x*.1;

                          %C#1%

                          [i*0 s*.2];
  MODEL:
                          %OVERALL%
                            i BY y1-y4@1;
                          s BY y1@0 y2@1 y3@2 y4@3;
                          [y1-y4@0];
                          [i*0 s*.2];
                          i*.25;
                          s*.09;
                          i WITH s*0;
                          y1-y4*.5;

                          i ON x*.5;
                          s ON x*.1;

                          %C#1%

                          [i*0 s*.2];
  OUTPUT:                TECH9;



INPUT READING TERMINATED NORMALLY



growth4.inp  normal, covariate, no missing

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         600

Number of replications
    Requested                                                10000
    Completed                                                10000
Value of seed                                                53487

Number of dependent variables                                    4
Number of independent variables                                  1
Number of continuous latent variables                            2
Number of categorical latent variables                           1

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4

Observed independent variables
   X

Continuous latent variables
   I           S

Categorical latent variables
   C


Estimator                                                       ML
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
Optimization algorithm                                         EMA


SAMPLE STATISTICS FOR THE FIRST REPLICATION


     SAMPLE STATISTICS


           Means
              Y1            Y2            Y3            Y4            X
              ________      ________      ________      ________      ________
 1              0.252         0.500         0.719         0.946         0.470


           Covariances
              Y1            Y2            Y3            Y4            X
              ________      ________      ________      ________      ________
 Y1             0.843
 Y2             0.345         1.000
 Y3             0.371         0.633         1.301
 Y4             0.399         0.737         1.077         1.923
 X              0.138         0.154         0.183         0.205         0.250


           Correlations
              Y1            Y2            Y3            Y4            X
              ________      ________      ________      ________      ________
 Y1             1.000
 Y2             0.376         1.000
 Y3             0.354         0.555         1.000
 Y4             0.313         0.531         0.681         1.000
 X              0.300         0.309         0.322         0.296         1.000




TESTS OF MODEL FIT

Number of Free Parameters                       11

Loglikelihood

    H0 Value

        Mean                             -3165.742
        Std Dev                             34.668
        Number of successful computations    10000

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.990        -3246.392      -3246.836
           0.980       0.980        -3236.941      -3237.055
           0.950       0.951        -3222.768      -3222.390
           0.900       0.901        -3210.173      -3209.952
           0.800       0.798        -3194.919      -3195.197
           0.700       0.701        -3183.923      -3183.712
           0.500       0.501        -3165.742      -3165.682
           0.300       0.301        -3147.562      -3147.508
           0.200       0.202        -3136.566      -3136.338
           0.100       0.101        -3121.311      -3121.142
           0.050       0.049        -3108.716      -3109.269
           0.020       0.021        -3094.544      -3094.285
           0.010       0.011        -3085.093      -3084.392

Information Criteria

    Akaike (AIC)

        Mean                              6353.485
        Std Dev                             69.337
        Number of successful computations    10000

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.989         6192.187       6190.420
           0.980       0.979         6211.088       6210.539
           0.950       0.951         6239.433       6240.465
           0.900       0.899         6264.623       6264.212
           0.800       0.798         6295.131       6294.617
           0.700       0.699         6317.125       6316.978
           0.500       0.499         6353.485       6353.361
           0.300       0.299         6389.845       6389.400
           0.200       0.202         6411.839       6412.339
           0.100       0.099         6442.347       6441.859
           0.050       0.049         6467.537       6466.603
           0.020       0.020         6495.882       6496.052
           0.010       0.010         6514.783       6515.635

    Bayesian (BIC)

        Mean                              6401.851
        Std Dev                             69.337
        Number of successful computations    10000

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.989         6240.553       6238.786
           0.980       0.979         6259.454       6258.905
           0.950       0.951         6287.799       6288.831
           0.900       0.899         6312.989       6312.578
           0.800       0.798         6343.497       6342.983
           0.700       0.699         6365.491       6365.345
           0.500       0.499         6401.851       6401.727
           0.300       0.299         6438.211       6437.766
           0.200       0.202         6460.205       6460.706
           0.100       0.099         6490.713       6490.225
           0.050       0.049         6515.903       6514.970
           0.020       0.020         6544.248       6544.418
           0.010       0.010         6563.149       6564.001

    Sample-Size Adjusted BIC (n* = (n + 2) / 24)

        Mean                              6366.929
        Std Dev                             69.337
        Number of successful computations    10000

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.989         6205.631       6203.864
           0.980       0.979         6224.532       6223.983
           0.950       0.951         6252.877       6253.909
           0.900       0.899         6278.067       6277.656
           0.800       0.798         6308.575       6308.061
           0.700       0.699         6330.569       6330.423
           0.500       0.499         6366.929       6366.805
           0.300       0.299         6403.289       6402.844
           0.200       0.202         6425.283       6425.784
           0.100       0.099         6455.791       6455.303
           0.050       0.049         6480.981       6480.048
           0.020       0.020         6509.326       6509.496
           0.010       0.010         6528.227       6529.079



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

    Latent
   Classes

       1        600.00000          1.00000


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

    Latent
   Classes

       1        600.00000          1.00000


CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

    Latent
   Classes

       1              600          1.00000


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

           1

    1   1.000


MODEL RESULTS

                           ESTIMATES              S. E.     M. S. E.  95%  % Sig
              Population   Average   Std. Dev.   Average             Cover Coeff
Latent Class 1

 I        BY
  Y1               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  Y2               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  Y3               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  Y4               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000

 S        BY
  Y1               0.000     0.0000     0.0000     0.0000     0.0000 1.000 0.000
  Y2               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  Y3               2.000     2.0000     0.0000     0.0000     0.0000 1.000 0.000
  Y4               3.000     3.0000     0.0000     0.0000     0.0000 1.000 0.000

 I          ON
  X                0.500     0.5004     0.0638     0.0632     0.0041 0.943 1.000

 S          ON
  X                0.100     0.1002     0.0357     0.0356     0.0013 0.947 0.808

 I        WITH
  S                0.000     0.0002     0.0192     0.0190     0.0004 0.949 0.051

 Intercepts
  Y1               0.000     0.0000     0.0000     0.0000     0.0000 1.000 0.000
  Y2               0.000     0.0000     0.0000     0.0000     0.0000 1.000 0.000
  Y3               0.000     0.0000     0.0000     0.0000     0.0000 1.000 0.000
  Y4               0.000     0.0000     0.0000     0.0000     0.0000 1.000 0.000
  I                0.000    -0.0002     0.0451     0.0447     0.0020 0.947 0.053
  S                0.200     0.2001     0.0254     0.0251     0.0006 0.946 1.000

 Residual Variances
  Y1               0.500     0.4999     0.0488     0.0485     0.0024 0.950 1.000
  Y2               0.500     0.4999     0.0348     0.0352     0.0012 0.949 1.000
  Y3               0.500     0.4996     0.0400     0.0394     0.0016 0.945 1.000
  Y4               0.500     0.5002     0.0630     0.0628     0.0040 0.948 1.000
  I                0.250     0.2487     0.0432     0.0428     0.0019 0.948 1.000
  S                0.090     0.0896     0.0134     0.0133     0.0002 0.945 1.000


QUALITY OF NUMERICAL RESULTS

     Average Condition Number for the Information Matrix      0.459E-02
       (ratio of smallest to largest eigenvalue)


TECHNICAL 1 OUTPUT


     PARAMETER SPECIFICATION FOR LATENT CLASS 1


           NU
              Y1            Y2            Y3            Y4            X
              ________      ________      ________      ________      ________
 1                  0             0             0             0             0


           LAMBDA
              I             S             X
              ________      ________      ________
 Y1                 0             0             0
 Y2                 0             0             0
 Y3                 0             0             0
 Y4                 0             0             0
 X                  0             0             0


           THETA
              Y1            Y2            Y3            Y4            X
              ________      ________      ________      ________      ________
 Y1                 1
 Y2                 0             2
 Y3                 0             0             3
 Y4                 0             0             0             4
 X                  0             0             0             0             0


           ALPHA
              I             S             X
              ________      ________      ________
 1                  5             6             0


           BETA
              I             S             X
              ________      ________      ________
 I                  0             0             7
 S                  0             0             8
 X                  0             0             0


           PSI
              I             S             X
              ________      ________      ________
 I                  9
 S                 10            11
 X                  0             0             0


     PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1
              ________
 1                  0


           GAMMA(C)
              X
              ________
 C#1                0


     STARTING VALUES FOR LATENT CLASS 1


           NU
              Y1            Y2            Y3            Y4            X
              ________      ________      ________      ________      ________
 1              0.000         0.000         0.000         0.000         0.000


           LAMBDA
              I             S             X
              ________      ________      ________
 Y1             1.000         0.000         0.000
 Y2             1.000         1.000         0.000
 Y3             1.000         2.000         0.000
 Y4             1.000         3.000         0.000
 X              0.000         0.000         1.000


           THETA
              Y1            Y2            Y3            Y4            X
              ________      ________      ________      ________      ________
 Y1             0.500
 Y2             0.000         0.500
 Y3             0.000         0.000         0.500
 Y4             0.000         0.000         0.000         0.500
 X              0.000         0.000         0.000         0.000         0.000


           ALPHA
              I             S             X
              ________      ________      ________
 1              0.000         0.200         0.000


           BETA
              I             S             X
              ________      ________      ________
 I              0.000         0.000         0.500
 S              0.000         0.000         0.100
 X              0.000         0.000         0.000


           PSI
              I             S             X
              ________      ________      ________
 I              0.250
 S              0.000         0.090
 X              0.000         0.000         0.500


     STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1
              ________
 1              0.000


           GAMMA(C)
              X
              ________
 C#1            0.000


     POPULATION VALUES FOR LATENT CLASS 1


           NU
              Y1            Y2            Y3            Y4            X
              ________      ________      ________      ________      ________
 1              0.000         0.000         0.000         0.000         0.000


           LAMBDA
              I             S             X
              ________      ________      ________
 Y1             1.000         0.000         0.000
 Y2             1.000         1.000         0.000
 Y3             1.000         2.000         0.000
 Y4             1.000         3.000         0.000
 X              0.000         0.000         1.000


           THETA
              Y1            Y2            Y3            Y4            X
              ________      ________      ________      ________      ________
 Y1             0.500
 Y2             0.000         0.500
 Y3             0.000         0.000         0.500
 Y4             0.000         0.000         0.000         0.500
 X              0.000         0.000         0.000         0.000         0.000


           ALPHA
              I             S             X
              ________      ________      ________
 1              0.000         0.200         0.000


           BETA
              I             S             X
              ________      ________      ________
 I              0.000         0.000         0.500
 S              0.000         0.000         0.100
 X              0.000         0.000         0.000


           PSI
              I             S             X
              ________      ________      ________
 I              0.250
 S              0.000         0.090
 X              0.000         0.000         1.000


     POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1
              ________
 1              0.000


           GAMMA(C)
              X
              ________
 C#1            0.000


TECHNICAL 9 OUTPUT

  Error messages for each replication (if any)



SAVEDATA INFORMATION

  Order of variables

    Y1
    Y2
    Y3
    Y4
    X
    C

  Save file
    growth4.sav

  Save file format           Free
  Save file record length    5000


     Beginning Time:  23:18:17
        Ending Time:  23:19:23
       Elapsed Time:  00:01:06



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