Mplus VERSION 7.3
MUTHEN & MUTHEN
09/22/2014   5:49 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a LCA with
  	continuous latent class indicators using
  	user-specified starting values without
  	random starts
  DATA:	FILE IS ex7.10.dat;
  VARIABLE:	NAMES ARE y1-y4 c;
  	USEVARIABLES ARE y1-y4;
  	CLASSES = c (2);
  ANALYSIS:	TYPE = MIXTURE;
  	STARTS = 0;
  MODEL:	
  	%OVERALL%
  	%c#1%
  	[y1-y4*1];
  	y1-y4;
  	%c#2%
  	[y1-y4*-1];
  	y1-y4;
  OUTPUT:	TECH1 TECH8;



*** 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
user-specified starting values without
random starts

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

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

Input data file(s)
  ex7.10.dat
Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       17

Loglikelihood

          H0 Value                       -3174.564
          H0 Scaling Correction Factor      0.9595
            for MLR

Information Criteria

          Akaike (AIC)                    6383.127
          Bayesian (BIC)                  6454.776
          Sample-Size Adjusted BIC        6400.817
            (n* = (n + 2) / 24)



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

    Latent
   Classes

       1        260.17548          0.52035
       2        239.82452          0.47965


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

    Latent
   Classes

       1        260.17579          0.52035
       2        239.82421          0.47965


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

Class Counts and Proportions

    Latent
   Classes

       1              261          0.52200
       2              239          0.47800


CLASSIFICATION QUALITY

     Entropy                         0.909


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

           1        2

    1   0.975    0.025
    2   0.024    0.976


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

           1        2

    1   0.978    0.022
    2   0.027    0.973


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

              1        2

    1      3.796    0.000
    2     -3.574    0.000


MODEL RESULTS

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

Latent Class 1

 Means
    Y1                 1.040      0.071     14.744      0.000
    Y2                 1.006      0.064     15.781      0.000
    Y3                 0.867      0.068     12.771      0.000
    Y4                 0.980      0.060     16.201      0.000

 Variances
    Y1                 1.154      0.100     11.513      0.000
    Y2                 0.979      0.079     12.387      0.000
    Y3                 1.085      0.087     12.488      0.000
    Y4                 0.909      0.077     11.741      0.000

Latent Class 2

 Means
    Y1                -1.055      0.070    -15.035      0.000
    Y2                -1.087      0.067    -16.140      0.000
    Y3                -0.942      0.063    -15.062      0.000
    Y4                -1.089      0.075    -14.605      0.000

 Variances
    Y1                 1.110      0.101     10.970      0.000
    Y2                 0.965      0.098      9.878      0.000
    Y3                 0.888      0.090      9.876      0.000
    Y4                 1.124      0.103     10.900      0.000

Categorical Latent Variables

 Means
    C#1                0.081      0.095      0.853      0.393


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.179E+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                13
 Y2                 0            14
 Y3                 0             0            15
 Y4                 0             0             0            16


     PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1                 17             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             1.114
 Y2             0.000         1.033
 Y3             0.000         0.000         0.904
 Y4             0.000         0.000         0.000         1.040


     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             1.114
 Y2             0.000         1.033
 Y3             0.000         0.000         0.904
 Y4             0.000         0.000         0.000         1.040


     STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART


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


TECHNICAL 8 OUTPUT


  ITER  LOGLIKELIHOOD    ABS CHANGE   REL CHANGE      CLASS COUNTS    ALGORITHM
     1 -0.31831778D+04    0.0000000    0.0000000    256.620   243.380    EM
     2 -0.31748131D+04    8.3646798    0.0026278    258.516   241.484    EM
     3 -0.31746072D+04    0.2059063    0.0000649    259.374   240.626    EM
     4 -0.31745735D+04    0.0336986    0.0000106    259.786   240.214    EM
     5 -0.31745660D+04    0.0075237    0.0000024    259.986   240.014    EM
     6 -0.31745643D+04    0.0017591    0.0000006    260.084   239.916    EM
     7 -0.31745638D+04    0.0004155    0.0000001    260.131   239.869    EM
     8 -0.31745637D+04    0.0000983    0.0000000    260.154   239.846    EM
     9 -0.31745637D+04    0.0000234    0.0000000    260.165   239.835    EM
    10 -0.31745637D+04    0.0000056    0.0000000    260.171   239.829    EM
    11 -0.31745637D+04    0.0000013    0.0000000    260.174   239.826    EM
    12 -0.31745637D+04    0.0000003    0.0000000    260.175   239.825    EM
    13 -0.31745637D+04    0.0000001    0.0000000    260.175   239.825    EM
    14 -0.31745637D+04    0.0000000    0.0000000    260.176   239.824    EM


     Beginning Time:  17:49:24
        Ending Time:  17:49:24
       Elapsed Time:  00:00:00



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