Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  10:55 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a growth model with
  	individually-varying times of observation
  	and a random slope for time-varying
  	covariates for a continuous outcome
  DATA:	FILE IS ex6.12.dat;
  VARIABLE:	NAMES ARE y1-y4 x a21-a24 a11-a14;
  	TSCORES = a11-a14;
  ANALYSIS:	TYPE = RANDOM;
  MODEL:	i s | y1-y4 AT a11-a14;
  	st | y1 ON a21;
  	st | y2 ON a22;
  	st | y3 ON a23;
  	st | y4 ON a24;
  	i s st ON x;



INPUT READING TERMINATED NORMALLY



this is an example of a growth model with
individually-varying times of observation
and a random slope for time-varying
covariates for a continuous outcome

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    4
Number of independent variables                                  5
Number of continuous latent variables                            3

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4

Observed independent variables
   X           A21         A22         A23         A24

Continuous latent variables
   ST          I           S

Variables with special functions

  Time scores
   A11         A12         A13         A14


Estimator                                                      MLR
Information matrix                                        OBSERVED
Maximum number of iterations                                   100
Convergence criterion                                    0.100D-05
Maximum number of EM iterations                                500
Convergence criteria for the EM algorithm
  Loglikelihood change                                   0.100D-02
  Relative loglikelihood change                          0.100D-05
  Derivative                                             0.100D-03
Minimum variance                                         0.100D-03
Maximum number of steepest descent iterations                   20
Optimization algorithm                                         EMA

Input data file(s)
  ex6.12.dat
Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       14

Loglikelihood

          H0 Value                       -3166.918
          H0 Scaling Correction Factor      1.0203
            for MLR

Information Criteria

          Akaike (AIC)                    6361.835
          Bayesian (BIC)                  6420.840
          Sample-Size Adjusted BIC        6376.403
            (n* = (n + 2) / 24)



MODEL RESULTS

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

 I          ON
    X                  0.697      0.051     13.567      0.000

 S          ON
    X                  0.333      0.025     13.389      0.000

 ST         ON
    X                  0.156      0.035      4.411      0.000

 S        WITH
    I                  0.025      0.032      0.802      0.423

 Intercepts
    Y1                 0.000      0.000    999.000    999.000
    Y2                 0.000      0.000    999.000    999.000
    Y3                 0.000      0.000    999.000    999.000
    Y4                 0.000      0.000    999.000    999.000
    ST                 0.453      0.036     12.706      0.000
    I                  0.463      0.051      9.043      0.000
    S                  1.007      0.025     40.749      0.000

 Residual Variances
    Y1                 0.468      0.064      7.347      0.000
    Y2                 0.501      0.058      8.674      0.000
    Y3                 0.405      0.058      7.035      0.000
    Y4                 0.556      0.092      6.044      0.000
    ST                 0.366      0.041      8.997      0.000
    I                  0.870      0.082     10.645      0.000
    S                  0.175      0.020      8.785      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.176E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:55:46
        Ending Time:  22:55:47
       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) 1998-2012 Muthen & Muthen

Back to examples