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

INPUT INSTRUCTIONS

  TITLE:	this is an example of a discrete-time
  survival analysis
  DATA:	FILE IS ex6.19.dat;
  VARIABLE:	NAMES ARE u1-u4 x;
  	CATEGORICAL = u1-u4;
  	MISSING = ALL (999);
  ANALYSIS: ESTIMATOR = MLR;
  MODEL:	f BY u1-u4@1;
  	f ON x;
  	f@0;



INPUT READING TERMINATED NORMALLY



this is an example of a discrete-time
survival analysis

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    4
Number of independent variables                                  1
Number of continuous latent variables                            1

Observed dependent variables

  Binary and ordered categorical (ordinal)
   U1          U2          U3          U4

Observed independent variables
   X

Continuous latent variables
   F


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                            0
  Adaptive quadrature                                           ON
Link                                                         LOGIT
Cholesky                                                        ON

Input data file(s)
  ex6.19.dat
Input data format  FREE


SUMMARY OF DATA

     Number of missing data patterns             7


COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100


     PROPORTION OF DATA PRESENT FOR U


           Covariance Coverage
              U1            U2            U3            U4
              ________      ________      ________      ________
 U1             0.522
 U2             0.452         0.930
 U3             0.334         0.732         0.732
 U4             0.224         0.520         0.520         0.520


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    U1
      Category 1    0.866      226.000
      Category 2    0.134       35.000
    U2
      Category 1    0.787      366.000
      Category 2    0.213       99.000
    U3
      Category 1    0.710      260.000
      Category 2    0.290      106.000
    U4
      Category 1    0.758      197.000
      Category 2    0.242       63.000



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        5

Loglikelihood

          H0 Value                        -678.342
          H0 Scaling Correction Factor      0.9821
            for MLR

Information Criteria

          Akaike (AIC)                    1366.683
          Bayesian (BIC)                  1387.756
          Sample-Size Adjusted BIC        1371.886
            (n* = (n + 2) / 24)



MODEL RESULTS

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

 F        BY
    U1                 1.000      0.000    999.000    999.000
    U2                 1.000      0.000    999.000    999.000
    U3                 1.000      0.000    999.000    999.000
    U4                 1.000      0.000    999.000    999.000

 F          ON
    X                  0.535      0.069      7.756      0.000

 Thresholds
    U1$1               1.997      0.187     10.685      0.000
    U2$1               1.383      0.119     11.629      0.000
    U3$1               0.902      0.119      7.545      0.000
    U4$1               1.083      0.145      7.467      0.000

 Residual Variances
    F                  0.000      0.000    999.000    999.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.136E+00
       (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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