Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010  11:24 PM

INPUT INSTRUCTIONS

  title: jasaa.inp

  montecarlo:
      names are y1 y2 y3 x;
      nobs = 2000;
      nreps = 500;
      seed = 578243;
      classes = c(2);
      genclasses = c(2);
      cutpoints = x(0);

  analysis:
      type = mixture;

  model montecarlo:
      %overall%
      [x@0]; x@1;

      i by y1-y3@1;
      s by y1@0 y2@1 y3@2;
      [y1-y3@0 i*0 s*1];
      i*6.25; ! SD = 2.5
      s*1;
      !total s variance is 1.25 (1/5 of i variance),  SD = 1.12
      i with s*.699; !this gives correlation 0.25
      y1*2.083 y2*0.417 y3*.417; !this gives y1 and y2 r-square 0.75
      ! y1 variance = 8.333, SD = 2.89
      ! y2 variance = 9.135, SD = 3.052
      ! within-group y2 variance = 9.135 - 0.25 = 9.065, SD = 3.01

      s on x*1;
      !this gives ES = 0.33 in y2 within-group SD terms (for low class)
      !r-squared for s is 20%

      [c#1*0];

      %c#1% ! high class
      [i*2.5 s*2.25];
      !high class grows at 3/4 SD per grade
      s on x*.25;
      ! high class has 1/4 effect of low class, ES = 0.08


      %c#2% !low class
      [i*0.0 s*.75];
      !low class grows at 1/4 SD per grade
      !low class is lower by 1 SD for intercept,
      !about 1.5 SD lower for slope



  model:
      %overall%

      i by y1-y3@1;
      s by y1@0 y2@1 y3@2;
      [y1-y3@0 i*0 s*1];
      i*6.25; ! SD = 2.5
      s*1;
      !total s variance is 1.25 (1/5 of i variance),  SD = 1.12
      i with s*.699;
      y1*2.083 y2*0.417 y3*.417; !this gives y1 and y2 r-square 0.75
      ! y1 variance = 8.333, SD = 2.89
      ! y2 variance = 9.135, SD = 3.052
      ! within-group y2 variance = 9.135 - 0.25 = 9.065, SD = 3.01

      s on x*1;
      !this gives ES = 0.33 in y2 within-group SD terms (for low class)
      !r-squared for s is 20%

      [c#1*0];

      %c#1% ! high class
      [i*2.5 s*2.25];
      !high class grows at 3/4 SD per grade
      s on x*.25;
      ! high class has 1/4 effect of low class, ES = 0.08


      %c#2% !low class
      [i*0.0 s*.75];
      !low class grows at 1/4 SD per grade
      !low class is lower by 1 SD for intercept,
      !about 1.5 SD lower for slope

  output:
      tech9;



INPUT READING TERMINATED NORMALLY



jasaa.inp

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        2000

Number of replications
    Requested                                                  500
    Completed                                                  500
Value of seed                                               578243

Number of dependent variables                                    3
Number of independent variables                                  1
Number of continuous latent variables                            2
Number of categorical latent variables                           1

Observed dependent variables

  Continuous
   Y1          Y2          Y3

Observed independent variables
   X

Continuous latent variables
   I           S

Categorical latent variables
   C


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
Optimization algorithm                                         EMA


SAMPLE STATISTICS FOR THE FIRST REPLICATION


     SAMPLE STATISTICS


           Means
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 1              1.186         3.002         4.834         0.501


           Covariances
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 Y1             9.809
 Y2             9.162        12.481
 Y3            10.636        14.972        19.636
 X              0.006         0.185         0.334         0.250


           Correlations
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 Y1             1.000
 Y2             0.828         1.000
 Y3             0.766         0.956         1.000
 X              0.004         0.105         0.151         1.000




TESTS OF MODEL FIT

Number of Free Parameters                       13

Loglikelihood

    H0 Value

        Mean                            -12660.705
        Std Dev                             52.289
        Number of successful computations      500

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.990       -12782.346     -12782.752
           0.980       0.980       -12768.092     -12769.627
           0.950       0.958       -12746.716     -12746.087
           0.900       0.902       -12727.719     -12728.162
           0.800       0.802       -12704.712     -12704.870
           0.700       0.704       -12688.126     -12687.859
           0.500       0.498       -12660.705     -12661.844
           0.300       0.298       -12633.285     -12634.190
           0.200       0.202       -12616.699     -12616.557
           0.100       0.104       -12593.691     -12590.140
           0.050       0.054       -12574.695     -12573.730
           0.020       0.022       -12553.319     -12551.106
           0.010       0.010       -12539.065     -12539.334

Information Criteria

    Akaike (AIC)

        Mean                             25347.411
        Std Dev                            104.578
        Number of successful computations      500

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.990        25104.130      25101.419
           0.980       0.978        25132.638      25125.896
           0.950       0.946        25175.390      25170.306
           0.900       0.896        25213.383      25203.243
           0.800       0.798        25259.397      25258.045
           0.700       0.702        25292.570      25293.265
           0.500       0.502        25347.411      25347.990
           0.300       0.296        25402.251      25401.680
           0.200       0.198        25435.424      25435.226
           0.100       0.098        25481.438      25478.177
           0.050       0.042        25519.432      25517.553
           0.020       0.020        25562.183      25562.120
           0.010       0.010        25590.691      25575.430

    Bayesian (BIC)

        Mean                             25420.222
        Std Dev                            104.578
        Number of successful computations      500

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.990        25176.942      25174.231
           0.980       0.978        25205.450      25198.708
           0.950       0.946        25248.201      25243.118
           0.900       0.896        25286.195      25276.054
           0.800       0.798        25332.209      25330.857
           0.700       0.702        25365.381      25366.077
           0.500       0.502        25420.222      25420.802
           0.300       0.296        25475.063      25474.492
           0.200       0.198        25508.235      25508.038
           0.100       0.098        25554.250      25550.989
           0.050       0.042        25592.243      25590.365
           0.020       0.020        25634.995      25634.932
           0.010       0.010        25663.503      25648.241

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

        Mean                             25378.921
        Std Dev                            104.578
        Number of successful computations      500

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.990        25135.640      25132.929
           0.980       0.978        25164.148      25157.406
           0.950       0.946        25206.900      25201.816
           0.900       0.896        25244.893      25234.753
           0.800       0.798        25290.907      25289.555
           0.700       0.702        25324.080      25324.775
           0.500       0.502        25378.921      25379.500
           0.300       0.296        25433.761      25433.190
           0.200       0.198        25466.934      25466.736
           0.100       0.098        25512.948      25509.687
           0.050       0.042        25550.942      25549.063
           0.020       0.020        25593.693      25593.630
           0.010       0.010        25622.201      25606.940



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

    Latent
   Classes

       1       1004.79656          0.50240
       2        995.20344          0.49760


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

    Latent
   Classes

       1       1004.79659          0.50240
       2        995.20341          0.49760


CLASSIFICATION QUALITY

     Entropy                         0.296


CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

    Latent
   Classes

       1             1008          0.50379
       2              992          0.49621


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

           1        2

    1   0.752    0.248
    2   0.249    0.751


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

 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

 S          ON
  X                0.250     0.2013     0.2459     0.1847     0.0627 0.928 0.488

 I        WITH
  S                0.699     0.7507     0.2444     0.2353     0.0623 0.884 0.910

 Means
  I                2.500     2.4366     0.3762     0.3542     0.1452 0.884 0.970

 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
  S                2.250     2.2624     0.1577     0.1601     0.0250 0.924 1.000

 Variances
  I                6.250     6.3139     0.6974     0.6875     0.4895 0.852 0.998

 Residual Variances
  Y1               2.083     2.0734     0.1515     0.1505     0.0230 0.944 1.000
  Y2               0.417     0.4222     0.0794     0.0805     0.0063 0.954 1.000
  Y3               0.417     0.4090     0.1947     0.1993     0.0379 0.964 0.532
  S                1.000     0.9828     0.1440     0.1647     0.0210 0.940 0.992

Latent Class 2

 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

 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

 S          ON
  X                1.000     1.0605     0.2436     0.1923     0.0629 0.932 0.982

 I        WITH
  S                0.699     0.7507     0.2444     0.2353     0.0623 0.884 0.910

 Means
  I                0.000     0.0521     0.3542     0.3528     0.1279 0.898 0.102

 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
  S                0.750     0.7236     0.1742     0.1670     0.0310 0.936 0.936

 Variances
  I                6.250     6.3139     0.6974     0.6875     0.4895 0.852 0.998

 Residual Variances
  Y1               2.083     2.0734     0.1515     0.1505     0.0230 0.944 1.000
  Y2               0.417     0.4222     0.0794     0.0805     0.0063 0.954 1.000
  Y3               0.417     0.4090     0.1947     0.1993     0.0379 0.964 0.532
  S                1.000     0.9828     0.1440     0.1647     0.0210 0.940 0.992

Categorical Latent Variables

 Means
  C#1              0.000     0.0135     0.3599     0.3305     0.1295 0.950 0.050


QUALITY OF NUMERICAL RESULTS

     Average Condition Number for the Information Matrix      0.330E-02
       (ratio of smallest to largest eigenvalue)


TECHNICAL 1 OUTPUT


     PARAMETER SPECIFICATION FOR LATENT CLASS 1


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


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


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


           ALPHA
              I             S             X
              ________      ________      ________
 1                  4             5             0


           BETA
              I             S             X
              ________      ________      ________
 I                  0             0             0
 S                  0             0             6
 X                  0             0             0


           PSI
              I             S             X
              ________      ________      ________
 I                  7
 S                  8             9
 X                  0             0             0


     PARAMETER SPECIFICATION FOR LATENT CLASS 2


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


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


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


           ALPHA
              I             S             X
              ________      ________      ________
 1                 10            11             0


           BETA
              I             S             X
              ________      ________      ________
 I                  0             0             0
 S                  0             0            12
 X                  0             0             0


           PSI
              I             S             X
              ________      ________      ________
 I                  7
 S                  8             9
 X                  0             0             0


     PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1                 13             0


           GAMMA(C)
              X
              ________
 C#1                0
 C#2                0


     STARTING VALUES FOR LATENT CLASS 1


           NU
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 1              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
 X              0.000         0.000         1.000


           THETA
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 Y1             2.083
 Y2             0.000         0.417
 Y3             0.000         0.000         0.417
 X              0.000         0.000         0.000         0.000


           ALPHA
              I             S             X
              ________      ________      ________
 1              2.500         2.250         0.000


           BETA
              I             S             X
              ________      ________      ________
 I              0.000         0.000         0.000
 S              0.000         0.000         0.250
 X              0.000         0.000         0.000


           PSI
              I             S             X
              ________      ________      ________
 I              6.250
 S              0.699         1.000
 X              0.000         0.000         0.500


     STARTING VALUES FOR LATENT CLASS 2


           NU
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 1              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
 X              0.000         0.000         1.000


           THETA
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 Y1             2.083
 Y2             0.000         0.417
 Y3             0.000         0.000         0.417
 X              0.000         0.000         0.000         0.000


           ALPHA
              I             S             X
              ________      ________      ________
 1              0.000         0.750         0.000


           BETA
              I             S             X
              ________      ________      ________
 I              0.000         0.000         0.000
 S              0.000         0.000         1.000
 X              0.000         0.000         0.000


           PSI
              I             S             X
              ________      ________      ________
 I              6.250
 S              0.699         1.000
 X              0.000         0.000         0.500


     STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1              0.000         0.000


           GAMMA(C)
              X
              ________
 C#1            0.000
 C#2            0.000


     POPULATION VALUES FOR LATENT CLASS 1


           NU
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 1              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
 X              0.000         0.000         1.000


           THETA
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 Y1             2.083
 Y2             0.000         0.417
 Y3             0.000         0.000         0.417
 X              0.000         0.000         0.000         0.000


           ALPHA
              I             S             X
              ________      ________      ________
 1              2.500         2.250         0.000


           BETA
              I             S             X
              ________      ________      ________
 I              0.000         0.000         0.000
 S              0.000         0.000         0.250
 X              0.000         0.000         0.000


           PSI
              I             S             X
              ________      ________      ________
 I              6.250
 S              0.699         1.000
 X              0.000         0.000         1.000


     POPULATION VALUES FOR LATENT CLASS 2


           NU
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 1              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
 X              0.000         0.000         1.000


           THETA
              Y1            Y2            Y3            X
              ________      ________      ________      ________
 Y1             2.083
 Y2             0.000         0.417
 Y3             0.000         0.000         0.417
 X              0.000         0.000         0.000         0.000


           ALPHA
              I             S             X
              ________      ________      ________
 1              0.000         0.750         0.000


           BETA
              I             S             X
              ________      ________      ________
 I              0.000         0.000         0.000
 S              0.000         0.000         1.000
 X              0.000         0.000         0.000


           PSI
              I             S             X
              ________      ________      ________
 I              6.250
 S              0.699         1.000
 X              0.000         0.000         1.000


     POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1              0.000         0.000


           GAMMA(C)
              X
              ________
 C#1            0.000
 C#2            0.000


TECHNICAL 9 OUTPUT

  Error messages for each replication (if any)


     REPLICATION 31:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 31:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 86:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 86:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 128:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 128:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 140:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 140:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 213:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 213:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 271:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 271:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 358:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 358:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 360:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 360:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 439:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 439:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 471:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 1 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.


     REPLICATION 471:
     WARNING:  THE RESIDUAL COVARIANCE MATRIX (THETA)  IN CLASS 2 IS NOT
     POSITIVE DEFINITE.  THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
     VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
     BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
     OBSERVED VARIABLES.  CHECK THE RESULTS SECTION FOR MORE INFORMATION.
     PROBLEM INVOLVING VARIABLE Y3.



     Beginning Time:  23:24:25
        Ending Time:  23:25:58
       Elapsed Time:  00:01:33



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