Mplus VERSION 7.3
MUTHEN & MUTHEN
09/22/2014   5:18 PM

INPUT INSTRUCTIONS

  title:
  	this is an example of a LCA with
  	continuous latent class indicators using
  	automatic starting values with random
  	starts

  montecarlo:
  	names are y1-y4;
  	genclasses = c(2);
  	classes = c(2);
  	nobs = 500;
  	seed = 3454367;
  	nrep = 1;
  	save = ex7.9.dat;

  analysis:
  	type = mixture;

  model population:

  	%overall%

  	[c#1*0];
  	
  	%c#1%
  	[y1-y4*1];
  	y1-y4*1;

  	%c#2%
  	[y1-y4*-1];
  	y1-y4*1;

  model:

  	%overall%

  	[c#1*0];

  	y1-y4*1;
  	
  	%c#1%
  	[y1-y4*1];


  	%c#2%
  	[y1-y4*-1];


  output:
  	tech8 tech9;
  	
  	
  	

  	
  	



*** 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




this is an example of a LCA with
continuous latent class indicators using
automatic starting values with random
starts

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of replications
    Requested                                                    1
    Completed                                                    1
Value of seed                                              3454367

Number of dependent variables                                    4
Number of independent variables                                  0
Number of continuous latent variables                            0
Number of categorical latent variables                           1

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4

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            Y4
              ________      ________      ________      ________
 1              0.035         0.002        -0.001        -0.012


           Covariances
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1             2.228
 Y2             1.100         2.066
 Y3             0.979         0.938         1.808
 Y4             1.033         1.052         0.918         2.080


           Correlations
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1             1.000
 Y2             0.513         1.000
 Y3             0.488         0.485         1.000
 Y4             0.480         0.507         0.474         1.000




MODEL FIT INFORMATION

Number of Free Parameters                       13

Loglikelihood

    H0 Value

        Mean                             -3177.162
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000        -3177.162      -3177.162
           0.980       0.000        -3177.162      -3177.162
           0.950       0.000        -3177.162      -3177.162
           0.900       0.000        -3177.162      -3177.162
           0.800       0.000        -3177.162      -3177.162
           0.700       0.000        -3177.162      -3177.162
           0.500       0.000        -3177.162      -3177.162
           0.300       0.000        -3177.162      -3177.162
           0.200       0.000        -3177.162      -3177.162
           0.100       0.000        -3177.162      -3177.162
           0.050       0.000        -3177.162      -3177.162
           0.020       0.000        -3177.162      -3177.162
           0.010       0.000        -3177.162      -3177.162

Information Criteria

    Akaike (AIC)

        Mean                              6380.324
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000         6380.324       6380.324
           0.980       0.000         6380.324       6380.324
           0.950       0.000         6380.324       6380.324
           0.900       0.000         6380.324       6380.324
           0.800       0.000         6380.324       6380.324
           0.700       0.000         6380.324       6380.324
           0.500       0.000         6380.324       6380.324
           0.300       0.000         6380.324       6380.324
           0.200       0.000         6380.324       6380.324
           0.100       0.000         6380.324       6380.324
           0.050       0.000         6380.324       6380.324
           0.020       0.000         6380.324       6380.324
           0.010       0.000         6380.324       6380.324

    Bayesian (BIC)

        Mean                              6435.114
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000         6435.114       6435.114
           0.980       0.000         6435.114       6435.114
           0.950       0.000         6435.114       6435.114
           0.900       0.000         6435.114       6435.114
           0.800       0.000         6435.114       6435.114
           0.700       0.000         6435.114       6435.114
           0.500       0.000         6435.114       6435.114
           0.300       0.000         6435.114       6435.114
           0.200       0.000         6435.114       6435.114
           0.100       0.000         6435.114       6435.114
           0.050       0.000         6435.114       6435.114
           0.020       0.000         6435.114       6435.114
           0.010       0.000         6435.114       6435.114

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

        Mean                              6393.851
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000         6393.851       6393.851
           0.980       0.000         6393.851       6393.851
           0.950       0.000         6393.851       6393.851
           0.900       0.000         6393.851       6393.851
           0.800       0.000         6393.851       6393.851
           0.700       0.000         6393.851       6393.851
           0.500       0.000         6393.851       6393.851
           0.300       0.000         6393.851       6393.851
           0.200       0.000         6393.851       6393.851
           0.100       0.000         6393.851       6393.851
           0.050       0.000         6393.851       6393.851
           0.020       0.000         6393.851       6393.851
           0.010       0.000         6393.851       6393.851



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

    Latent
   Classes

       1        260.61724          0.52123
       2        239.38276          0.47877


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

    Latent
   Classes

       1        260.61742          0.52123
       2        239.38258          0.47877


FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

    Latent
   Classes

       1              262          0.52400
       2              238          0.47600


CLASSIFICATION QUALITY

     Entropy                         0.909


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

           1        2

    1   0.974    0.026
    2   0.023    0.977


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

           1        2

    1   0.979    0.021
    2   0.028    0.972


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

              1        2

    1      3.853    0.000
    2     -3.533    0.000


MODEL RESULTS

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

 Means
  Y1                  1.000     1.0373     0.0000     0.0703     0.0014 1.000 1.000
  Y2                  1.000     1.0036     0.0000     0.0640     0.0000 1.000 1.000
  Y3                  1.000     0.8649     0.0000     0.0678     0.0183 0.000 1.000
  Y4                  1.000     0.9805     0.0000     0.0601     0.0004 1.000 1.000

 Variances
  Y1                  1.000     1.1345     0.0000     0.0727     0.0181 1.000 1.000
  Y2                  1.000     0.9746     0.0000     0.0625     0.0006 1.000 1.000
  Y3                  1.000     0.9918     0.0000     0.0641     0.0001 1.000 1.000
  Y4                  1.000     1.0072     0.0000     0.0642     0.0001 1.000 1.000

Latent Class 2

 Means
  Y1                 -1.000    -1.0562     0.0000     0.0703     0.0032 1.000 1.000
  Y2                 -1.000    -1.0877     0.0000     0.0669     0.0077 1.000 1.000
  Y3                 -1.000    -0.9431     0.0000     0.0627     0.0032 1.000 1.000
  Y4                 -1.000    -1.0931     0.0000     0.0744     0.0087 1.000 1.000

 Variances
  Y1                  1.000     1.1345     0.0000     0.0727     0.0181 1.000 1.000
  Y2                  1.000     0.9746     0.0000     0.0625     0.0006 1.000 1.000
  Y3                  1.000     0.9918     0.0000     0.0641     0.0001 1.000 1.000
  Y4                  1.000     1.0072     0.0000     0.0642     0.0001 1.000 1.000

Categorical Latent Variables

 Means
  C#1                 0.000     0.0850     0.0000     0.0956     0.0072 1.000 0.000


QUALITY OF NUMERICAL RESULTS

     Average Condition Number for the Information Matrix      0.328E+00
       (ratio of smallest to largest eigenvalue)


TECHNICAL 1 OUTPUT


     PARAMETER SPECIFICATION FOR LATENT CLASS 1


           NU
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 1                  1             2             3             4


           THETA
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1                 5
 Y2                 0             6
 Y3                 0             0             7
 Y4                 0             0             0             8


     PARAMETER SPECIFICATION FOR LATENT CLASS 2


           NU
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 1                  9            10            11            12


           THETA
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1                 5
 Y2                 0             6
 Y3                 0             0             7
 Y4                 0             0             0             8


     PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART


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


     STARTING VALUES FOR LATENT CLASS 1


           NU
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 1              1.000         1.000         1.000         1.000


           THETA
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1             1.000
 Y2             0.000         1.000
 Y3             0.000         0.000         1.000
 Y4             0.000         0.000         0.000         1.000


     STARTING VALUES FOR LATENT CLASS 2


           NU
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 1             -1.000        -1.000        -1.000        -1.000


           THETA
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1             1.000
 Y2             0.000         1.000
 Y3             0.000         0.000         1.000
 Y4             0.000         0.000         0.000         1.000


     STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART


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


     POPULATION VALUES FOR LATENT CLASS 1


           NU
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 1              1.000         1.000         1.000         1.000


           THETA
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1             1.000
 Y2             0.000         1.000
 Y3             0.000         0.000         1.000
 Y4             0.000         0.000         0.000         1.000


     POPULATION VALUES FOR LATENT CLASS 2


           NU
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 1             -1.000        -1.000        -1.000        -1.000


           THETA
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1             1.000
 Y2             0.000         1.000
 Y3             0.000         0.000         1.000
 Y4             0.000         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


TECHNICAL 8 OUTPUT


  TECHNICAL 8 OUTPUT FOR REPLICATION 1


  ITER  LOGLIKELIHOOD    ABS CHANGE   REL CHANGE      CLASS COUNTS    ALGORITHM
     1 -0.31839188D+04    0.0000000    0.0000000    256.827   243.173    EM
     2 -0.31774131D+04    6.5057125    0.0020433    259.025   240.975    EM
     3 -0.31772025D+04    0.2105896    0.0000663    259.941   240.059    EM
     4 -0.31771691D+04    0.0334158    0.0000105    260.329   239.671    EM
     5 -0.31771632D+04    0.0058718    0.0000018    260.494   239.506    EM
     6 -0.31771622D+04    0.0010572    0.0000003    260.565   239.435    EM
     7 -0.31771620D+04    0.0001920    0.0000001    260.595   239.405    EM
     8 -0.31771620D+04    0.0000350    0.0000000    260.608   239.392    EM
     9 -0.31771619D+04    0.0000064    0.0000000    260.613   239.387    EM
    10 -0.31771619D+04    0.0000012    0.0000000    260.616   239.384    EM
    11 -0.31771619D+04    0.0000002    0.0000000    260.617   239.383    EM
    12 -0.31771619D+04    0.0000000    0.0000000    260.617   239.383    EM
    13 -0.31771619D+04    0.0000000    0.0000000    260.617   239.383    EM


TECHNICAL 9 OUTPUT

  Error messages for each replication (if any)



SAVEDATA INFORMATION

  Order of variables

    Y1
    Y2
    Y3
    Y4
    C

  Save file
    ex7.9.dat

  Save file format           Free
  Save file record length    10000


     Beginning Time:  17:18:45
        Ending Time:  17:18:45
       Elapsed Time:  00:00:00



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