Mplus VERSION 7.3
MUTHEN & MUTHEN
09/22/2014   5:48 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a two-part (semicontinuous) growth model for a
          continuous outcome
  DATA:	FILE = ex6.16.dat;
  DATA TWOPART:
  	NAMES = y1-y4;
  	BINARY = bin1-bin4;
  	CONTINUOUS = cont1-cont4;
  VARIABLE:	NAMES = x y1-y4;
  	USEVARIABLES = bin1-bin4 cont1-cont4;
  	CATEGORICAL = bin1-bin4;
  	MISSING = ALL(999);
  ANALYSIS:	ESTIMATOR = MLR;
  MODEL:	iu su | bin1@0 bin2@1 bin3@2 bin4@3;
  	iy sy | cont1@0 cont2@1 cont3@2 cont4@3;
  	su@0; iu WITH sy@0;



*** WARNING in MODEL command
  All continuous latent variable covariances involving SU have been fixed to 0
  because the variance of SU is fixed at 0.
   1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS



this is an example of a two-part (semicontinuous) growth model for a
continuous outcome

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    8
Number of independent variables                                  0
Number of continuous latent variables                            4

Observed dependent variables

  Continuous
   CONT1       CONT2       CONT3       CONT4

  Binary and ordered categorical (ordinal)
   BIN1        BIN2        BIN3        BIN4

Continuous latent variables
   IU          SU          IY          SY


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
Maximum number of iterations for H1                           2000
Convergence criterion for H1                             0.100D-03
Optimization algorithm                                         EMA
Integration Specifications
  Type                                                    STANDARD
  Number of integration points                                  15
  Dimensions of numerical integration                            1
  Adaptive quadrature                                           ON
Link                                                         LOGIT
Cholesky                                                       OFF

Input data file(s)
  ex6.16.dat
Input data format  FREE


SUMMARY OF DATA

     Number of missing data patterns            16


COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100


     PROPORTION OF DATA PRESENT FOR Y


           Covariance Coverage
              CONT1         CONT2         CONT3         CONT4
              ________      ________      ________      ________
 CONT1          0.598
 CONT2          0.490         0.740
 CONT3          0.550         0.670         0.866
 CONT4          0.574         0.698         0.810         0.918


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    BIN1
      Category 1    0.402      201.000
      Category 2    0.598      299.000
    BIN2
      Category 1    0.260      130.000
      Category 2    0.740      370.000
    BIN3
      Category 1    0.134       67.000
      Category 2    0.866      433.000
    BIN4
      Category 1    0.082       41.000
      Category 2    0.918      459.000



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       13

Loglikelihood

          H0 Value                       -3576.088
          H0 Scaling Correction Factor      1.0031
            for MLR

Information Criteria

          Akaike (AIC)                    7178.176
          Bayesian (BIC)                  7232.966
          Sample-Size Adjusted BIC        7191.703
            (n* = (n + 2) / 24)

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

          Pearson Chi-Square

          Value                              6.260
          Degrees of Freedom                    12
          P-Value                           0.9024

          Likelihood Ratio Chi-Square

          Value                              5.689
          Degrees of Freedom                    12
          P-Value                           0.9309



MODEL RESULTS

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

 IU       |
    BIN1               1.000      0.000    999.000    999.000
    BIN2               1.000      0.000    999.000    999.000
    BIN3               1.000      0.000    999.000    999.000
    BIN4               1.000      0.000    999.000    999.000

 SU       |
    BIN1               0.000      0.000    999.000    999.000
    BIN2               1.000      0.000    999.000    999.000
    BIN3               2.000      0.000    999.000    999.000
    BIN4               3.000      0.000    999.000    999.000

 IY       |
    CONT1              1.000      0.000    999.000    999.000
    CONT2              1.000      0.000    999.000    999.000
    CONT3              1.000      0.000    999.000    999.000
    CONT4              1.000      0.000    999.000    999.000

 SY       |
    CONT1              0.000      0.000    999.000    999.000
    CONT2              1.000      0.000    999.000    999.000
    CONT3              2.000      0.000    999.000    999.000
    CONT4              3.000      0.000    999.000    999.000

 IU       WITH
    SY                 0.000      0.000    999.000    999.000

 IY       WITH
    IU                 0.893      0.183      4.883      0.000

 SY       WITH
    IY                 0.582      0.058     10.011      0.000

 Means
    IU                 0.000      0.000    999.000    999.000
    SU                 0.880      0.068     12.979      0.000
    IY                 0.530      0.077      6.899      0.000
    SY                 1.026      0.035     29.554      0.000

 Intercepts
    CONT1              0.000      0.000    999.000    999.000
    CONT2              0.000      0.000    999.000    999.000
    CONT3              0.000      0.000    999.000    999.000
    CONT4              0.000      0.000    999.000    999.000

 Thresholds
    BIN1$1            -0.541      0.114     -4.755      0.000
    BIN2$1            -0.541      0.114     -4.755      0.000
    BIN3$1            -0.541      0.114     -4.755      0.000
    BIN4$1            -0.541      0.114     -4.755      0.000

 Variances
    IU                 1.803      0.330      5.465      0.000
    SU                 0.000      0.000    999.000    999.000
    IY                 1.905      0.174     10.974      0.000
    SY                 0.403      0.042      9.533      0.000

 Residual Variances
    CONT1              0.583      0.095      6.106      0.000
    CONT2              0.555      0.053     10.476      0.000
    CONT3              0.522      0.064      8.153      0.000
    CONT4              0.450      0.094      4.814      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.614E-02
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  17:48:46
        Ending Time:  17:48:46
       Elapsed Time:  00:00:00



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