Mplus VERSION 7.2
MUTHEN & MUTHEN
05/07/2014   2:42 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a two-group twin
  		model for categorical outcomes using
  		maximum likelihood and parameter constraints
  DATA:		FILE = ex7.28.dat;
  VARIABLE:	NAMES = u1 u2 dz;
  		CATEGORICAL = u1 u2;
  		CLASSES = cdz (2);
  		KNOWNCLASS = cdz (dz = 0 dz = 1);
  ANALYSIS:	TYPE = MIXTURE;
  		ALGORITHM = INTEGRATION;
  		LINK = PROBIT;
  MODEL:	%OVERALL%
  		[u1$1-u2$1] (1);
  		f1 BY u1;
  		f2 BY u2;
      		[f1-f2@0];
      		f1-f2 (varf);
      		%cdz#1%
      		f1 WITH f2(covmz);
      		%cdz#2%
      		f1 WITH f2(covdz);
  MODEL CONSTRAINT:
  		NEW(a c h);
   		varf = a**2 + c**2 + .001;
   		covmz = a**2 + c**2;
  		covdz = 0.5*a**2 + c**2;
   		h = a**2/(a**2 + c**2 + 1);



INPUT READING TERMINATED NORMALLY



this is an example of a two-group twin
model for categorical outcomes using
maximum likelihood and parameter constraints

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        5000

Number of dependent variables                                    2
Number of independent variables                                  0
Number of continuous latent variables                            2
Number of categorical latent variables                           1

Observed dependent variables

  Binary and ordered categorical (ordinal)
   U1          U2

Continuous latent variables
   F1          F2

Categorical latent variables
   CDZ

  Knownclass            CDZ


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                            10
  Minimum value for logit thresholds                           -10
  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                            2
  Adaptive quadrature                                           ON
Link                                                        PROBIT
Cholesky                                                        ON

Input data file(s)
  ex7.28.dat
Input data format  FREE


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    U1
      Category 1    0.500     2501.000
      Category 2    0.500     2499.000
    U2
      Category 1    0.507     2535.000
      Category 2    0.493     2465.000



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        4

Loglikelihood

          H0 Value                       -9927.558
          H0 Scaling Correction Factor      0.9924
            for MLR

Information Criteria

          Akaike (AIC)                   19863.116
          Bayesian (BIC)                 19889.185
          Sample-Size Adjusted BIC       19876.474
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit for the Binary and Ordered Categorical
(Ordinal) Outcomes

          Pearson Chi-Square

          Value                              3.634
          Degrees of Freedom                     3
          P-Value                           0.3038

          Likelihood Ratio Chi-Square

          Value                              3.636
          Degrees of Freedom                     3
          P-Value                           0.3035



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

    Latent
   Classes

       1       2566.00000          0.51320
       2       2434.00000          0.48680


MODEL RESULTS

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

Latent Class 1 (0)

 F1       BY
    U1                 1.000      0.000    999.000    999.000

 F2       BY
    U2                 1.000      0.000    999.000    999.000

 F2       WITH
    F1                 2.931      0.262     11.203      0.000

 Means
    F1                 0.000      0.000    999.000    999.000
    F2                 0.000      0.000    999.000    999.000

 Thresholds
    U1$1               0.013      0.029      0.451      0.652
    U2$1               0.013      0.029      0.451      0.652

 Variances
    F1                 2.932      0.262     11.207      0.000
    F2                 2.932      0.262     11.207      0.000

Latent Class 2 (1)

 F1       BY
    U1                 1.000      0.000    999.000    999.000

 F2       BY
    U2                 1.000      0.000    999.000    999.000

 F2       WITH
    F1                 1.524      0.151     10.076      0.000

 Means
    F1                 0.000      0.000    999.000    999.000
    F2                 0.000      0.000    999.000    999.000

 Thresholds
    U1$1               0.013      0.029      0.451      0.652
    U2$1               0.013      0.029      0.451      0.652

 Variances
    F1                 2.932      0.262     11.207      0.000
    F2                 2.932      0.262     11.207      0.000

Categorical Latent Variables

 Means
    CDZ#1              0.053      0.028      1.867      0.062

New/Additional Parameters
    A                  1.678      0.116     14.453      0.000
    C                  0.341      0.338      1.009      0.313
    H                  0.716      0.066     10.857      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.795E-03
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  14:42:07
        Ending Time:  14:42:08
       Elapsed Time:  00:00:01



MUTHEN & MUTHEN
3463 Stoner Ave.
Los Angeles, CA  90066

Tel: (310) 391-9971
Fax: (310) 391-8971
Web: www.StatModel.com
Support: Support@StatModel.com

Copyright (c) 1998-2014 Muthen & Muthen

Back to examples