Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010  10:57 PM

INPUT INSTRUCTIONS

  ! SCRIPT NAME        : ordVCut2d (cvb)
  ! GOAL                : univariate Mx script for the analysis of one categorical phenotype
  ! DATA                : ordinal
  ! INPUT                : raw data
  ! UNI/BI/MULTI        : uni
  ! DATA-GROUPS        : MZ DZ
  ! MEANS MODEL        : assuming no differences in prevalences across twin1, twin2, males,
  ! VARIANCE COVARIANCE MODEL(S)        : 1. ADE 2. AE 3. E

  data: file is ordraw1.dat;

  variable: names are id y1 y2 zygot age;
            categorical=y1 y2;
            usevar are y1 y2;
            grouping=zygot(1=MZ 2=DZ);  ! specify the two groups MZ and DZ
            missing=all(-9); ! specify missing data symbol

  model:
       [y1$1 y2$1] (t);
       y1 with y2 (mzc);

  model dz:
       y1 with y2 (dzc);

  model constraint:

  ! Uncomment for Model ADE
    new(a d e x w z);
    a=x*x;
    d=w*w;
    e=1-x*x-w*w;
    z=sqrt(1-x*x-w*w);
    mzc=x*x+w*w;
    dzc=0.5*x*x+0.25*w*w;

  ! Uncomment for Model AE
  ! d=0;

  ! Uncomment for Model DE
  ! a=0;

  ! Uncomment for Model E
  ! a=0; d=0;



*** WARNING
  Data set contains unknown or missing values for GROUPING,
  PATTERN, COHORT, CLUSTER and/or STRATIFICATION variables.
  Number of cases with unknown or missing values:  2179
   1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS




SUMMARY OF ANALYSIS

Number of groups                                                 2
Number of observations
   Group MZ                                                    399
   Group DZ                                                    273

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

Observed dependent variables

  Binary and ordered categorical (ordinal)
   Y1          Y2

Variables with special functions

  Grouping variable     ZYGOT

Estimator                                                    WLSMV
Maximum number of iterations                                  1000
Convergence criterion                                    0.500D-04
Maximum number of steepest descent iterations                   20
Maximum number of iterations for H1                           2000
Convergence criterion for H1                             0.100D-03
Parameterization                                             DELTA

Input data file(s)
  ordraw1.dat

Input data format  FREE


SUMMARY OF DATA

   Group MZ
     Number of missing data patterns             3

   Group DZ
     Number of missing data patterns             3


COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100


     PROPORTION OF DATA PRESENT FOR MZ


           Covariance Coverage
              Y1            Y2
              ________      ________
 Y1             0.827
 Y2             0.609         0.782


     PROPORTION OF DATA PRESENT FOR DZ


           Covariance Coverage
              Y1            Y2
              ________      ________
 Y1             0.744
 Y2             0.498         0.755


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

  Group MZ
    Y1
      Category 1    0.952      314.000
      Category 2    0.048       16.000
    Y2
      Category 1    0.933      291.000
      Category 2    0.067       21.000

  Group DZ
    Y1
      Category 1    0.946      192.000
      Category 2    0.054       11.000
    Y2
      Category 1    0.937      193.000
      Category 2    0.063       13.000


     WARNING:  THE BIVARIATE TABLE OF Y2 AND Y1 HAS AN EMPTY CELL.


THE MODEL ESTIMATION TERMINATED NORMALLY



TESTS OF MODEL FIT

Chi-Square Test of Model Fit

          Value                              1.329*
          Degrees of Freedom                     3
          P-Value                           0.7222

Chi-Square Contributions From Each Group

          MZ                                 1.107
          DZ                                 0.222

*   The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
    for chi-square difference testing in the regular way.  MLM, MLR and WLSM
    chi-square difference testing is described on the Mplus website.  MLMV, WLSMV,
    and ULSMV difference testing is done using the DIFFTEST option.

Chi-Square Test of Model Fit for the Baseline Model

          Value                             22.404
          Degrees of Freedom                     2
          P-Value                           0.0000

CFI/TLI

          CFI                                1.000
          TLI                                1.055

Number of Free Parameters                        3

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.000

WRMR (Weighted Root Mean Square Residual)

          Value                              0.646



MODEL RESULTS

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

Group MZ

 Y1       WITH
    Y2                 0.648      0.137      4.719      0.000

 Thresholds
    Y1$1               1.569      0.066     23.702      0.000
    Y2$1               1.569      0.066     23.702      0.000

Group DZ

 Y1       WITH
    Y2                 0.162      0.581      0.279      0.780

 Thresholds
    Y1$1               1.569      0.066     23.702      0.000
    Y2$1               1.569      0.066     23.702      0.000

 New/Additional Parameters
    A                  0.000      2.327      0.000      1.000
    D                  0.648      2.339      0.277      0.782
    E                  0.352      0.137      2.561      0.010
    X                  0.009    127.414      0.000      1.000
    W                  0.805      1.453      0.554      0.579
    Z                  0.593      0.116      5.122      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.917E-07
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:57:40
        Ending Time:  22:57:40
       Elapsed Time:  00:00:00



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