Mplus VERSION 7.2
MUTHEN & MUTHEN
05/07/2014   2:24 PM

INPUT INSTRUCTIONS

  TITLE:this is an example of two-level mixture
  	regression for a continuous dependent variable
          with a between-level categorical latent variable
  DATA:	FILE = ex10.2.dat;
  VARIABLE:	NAMES ARE y x1 x2 w dummy clus;
  	USEVARIABLES = y-w;
  	CLASSES = cb(2);
  	WITHIN = x1 x2;
  	BETWEEN = cb w;
  	CLUSTER = clus;
  ANALYSIS:	TYPE = TWOLEVEL MIXTURE RANDOM;
  	PROCESSORS = 2;
  MODEL:
  	%WITHIN%
  	%OVERALL%
  	s1 | y ON x1;
  	s2 | y ON x2;
  	%BETWEEN%
  	%OVERALL%
  	cb y ON w; s1-s2@0;
  	%cb#1%
  	[s1 s2];
  	%cb#2%
  	[s1 s2];





INPUT READING TERMINATED NORMALLY



this is an example of two-level mixture
regression for a continuous dependent variable
with a between-level categorical latent variable

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        1000

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

Observed dependent variables

  Continuous
   Y

Observed independent variables
   X1          X2          W

Continuous latent variables
   S1          S2

Categorical latent variables
   CB

Variables with special functions

  Cluster variable      CLUS

  Within variables
   X1          X2

  Between variables
   W


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-02
    Relative loglikelihood change                        0.100D-05
    Derivative                                           0.100D-02
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-02
  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-02
  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
Integration Specifications
  Type                                                    STANDARD
  Number of integration points                                  15
  Dimensions of numerical integration                            1
  Adaptive quadrature                                           ON
Random Starts Specifications
  Number of initial stage random starts                         20
  Number of final stage optimizations                            4
  Number of initial stage iterations                            10
  Initial stage convergence criterion                    0.100D+01
  Random starts scale                                    0.500D+01
  Random seed for generating random starts                       0
Cholesky                                                       OFF

Input data file(s)
  ex10.2.dat
Input data format  FREE


SUMMARY OF DATA

     Number of clusters                        110



RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES

Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers:

           -1574.798  608496           4
           -1574.798  unperturbed      0
           -1574.800  27071            15
           -1574.800  637345           19



THE BEST LOGLIKELIHOOD VALUE HAS BEEN REPLICATED.  RERUN WITH AT LEAST TWICE THE
RANDOM STARTS TO CHECK THAT THE BEST LOGLIKELIHOOD IS STILL OBTAINED AND REPLICATED.


THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       11

Loglikelihood

          H0 Value                       -1574.798
          H0 Scaling Correction Factor      0.9503
            for MLR

Information Criteria

          Akaike (AIC)                    3171.596
          Bayesian (BIC)                  3225.582
          Sample-Size Adjusted BIC        3190.645
            (n* = (n + 2) / 24)



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

    Latent
   Classes

       1        496.53398          0.49653
       2        503.46602          0.50347


CLASSIFICATION QUALITY

     Entropy                         0.929


CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

    Latent
   Classes

       1              500          0.50000
       2              500          0.50000


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

           1        2

    1   0.975    0.025
    2   0.018    0.982


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

           1        2

    1   0.982    0.018
    2   0.025    0.975


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

              1        2

    1      3.972    0.000
    2     -3.659    0.000


MODEL RESULTS

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

Within Level

Latent Class 1

 Residual Variances
    Y                  1.005      0.046     21.800      0.000

Latent Class 2

 Residual Variances
    Y                  1.005      0.046     21.800      0.000

Between Level

Latent Class 1

 Y          ON
    W                  0.694      0.075      9.225      0.000

 Means
    S1                 0.972      0.042     23.353      0.000
    S2                 1.944      0.059     32.853      0.000

 Intercepts
    Y                  2.465      0.115     21.508      0.000

 Variances
    S1                 0.000      0.000    999.000    999.000
    S2                 0.000      0.000    999.000    999.000

 Residual Variances
    Y                  0.497      0.092      5.385      0.000

Latent Class 2

 Y          ON
    W                  0.694      0.075      9.225      0.000

 Means
    S1                 1.943      0.059     33.185      0.000
    S2                 1.018      0.042     23.970      0.000

 Intercepts
    Y                  0.804      0.119      6.777      0.000

 Variances
    S1                 0.000      0.000    999.000    999.000
    S2                 0.000      0.000    999.000    999.000

 Residual Variances
    Y                  0.497      0.092      5.385      0.000

Categorical Latent Variables

Within Level

Between Level

 CB#1       ON
    W                  0.822      0.217      3.783      0.000

 Intercepts
    CB#1              -0.068      0.217     -0.312      0.755


ALTERNATIVE PARAMETERIZATIONS FOR THE CATEGORICAL LATENT VARIABLE REGRESSION

Parameterization using Reference Class 1

 CB#2     ON
    W                 -0.822      0.217     -3.783      0.000

 Intercepts
    CB#2               0.068      0.217      0.312      0.755


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.203E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  14:24:58
        Ending Time:  14:25:01
       Elapsed Time:  00:00:03



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