Mplus VERSION 7.2
MUTHEN & MUTHEN
05/07/2014   2:34 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a linear growth model
  		with missing data on a continuous outcome
  		using a missing data correlate to improve
  		the plausibility of MAR
  DATA:		FILE = ex11.1.dat;
  VARIABLE:	NAMES = x1 y1-y4 z x2;
  		USEVARIABLES = y1-y4;
  		MISSING = ALL (999);
  		AUXILIARY = (m) z;
  ANALYSIS:	ESTIMATOR = ML;
  MODEL:	i s | y1@0 y2@1 y3@2 y4@3;
  OUTPUT:	TECH1;



INPUT READING TERMINATED NORMALLY



this is an example of a linear growth model
with missing data on a continuous outcome
using a missing data correlate to improve
the plausibility of MAR

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         200

Number of dependent variables                                    4
Number of independent variables                                  0
Number of continuous latent variables                            2

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4

Observed auxiliary variables
   Z

Continuous latent variables
   I           S


Estimator                                                       ML
Information matrix                                        OBSERVED
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

Input data file(s)
  ex11.1.dat

Input data format  FREE


SUMMARY OF DATA

     Number of missing data patterns            15


COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100


     PROPORTION OF DATA PRESENT


           Covariance Coverage
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1             0.710
 Y2             0.425         0.615
 Y3             0.390         0.355         0.525
 Y4             0.330         0.320         0.280         0.420



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        9

Loglikelihood Including the Auxiliary Part

          H0 Value                       -1009.197
          H1 Value                       -1007.232

Information Criteria Including the Auxiliary Part

          Number of Free Parameters             15
          Akaike (AIC)                    2048.394
          Bayesian (BIC)                  2097.869
          Sample-Size Adjusted BIC        2050.347
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                              3.930
          Degrees of Freedom                     5
          P-Value                           0.5595

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.000
          90 Percent C.I.                    0.000  0.087
          Probability RMSEA <= .05           0.776

CFI/TLI

          CFI                                1.000
          TLI                                1.005

Chi-Square Test of Model Fit for the Baseline Model

          Value                            276.533
          Degrees of Freedom                     6
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value                              0.047



MODEL RESULTS

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

 I        |
    Y1                 1.000      0.000    999.000    999.000
    Y2                 1.000      0.000    999.000    999.000
    Y3                 1.000      0.000    999.000    999.000
    Y4                 1.000      0.000    999.000    999.000

 S        |
    Y1                 0.000      0.000    999.000    999.000
    Y2                 1.000      0.000    999.000    999.000
    Y3                 2.000      0.000    999.000    999.000
    Y4                 3.000      0.000    999.000    999.000

 S        WITH
    I                  0.226      0.083      2.707      0.007

 Means
    I                  1.417      0.096     14.717      0.000
    S                  2.011      0.057     35.557      0.000

 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

 Variances
    I                  1.101      0.195      5.634      0.000
    S                  0.261      0.058      4.513      0.000

 Residual Variances
    Y1                 0.616      0.153      4.023      0.000
    Y2                 0.441      0.094      4.706      0.000
    Y3                 0.531      0.130      4.089      0.000
    Y4                 0.257      0.203      1.261      0.207


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.824E-02
       (ratio of smallest to largest eigenvalue)


TECHNICAL 1 OUTPUT


     PARAMETER SPECIFICATION


           NU
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
      1           0             0             0             0


           LAMBDA
              I             S
              ________      ________
 Y1                 0             0
 Y2                 0             0
 Y3                 0             0
 Y4                 0             0


           THETA
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1                 1
 Y2                 0             2
 Y3                 0             0             3
 Y4                 0             0             0             4


           ALPHA
              I             S
              ________      ________
      1           5             6


           BETA
              I             S
              ________      ________
 I                  0             0
 S                  0             0


           PSI
              I             S
              ________      ________
 I                  7
 S                  8             9


     STARTING VALUES


           NU
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
      1         0.000         0.000         0.000         0.000


           LAMBDA
              I             S
              ________      ________
 Y1             1.000         0.000
 Y2             1.000         1.000
 Y3             1.000         2.000
 Y4             1.000         3.000


           THETA
              Y1            Y2            Y3            Y4
              ________      ________      ________      ________
 Y1             0.616
 Y2             0.000         0.441
 Y3             0.000         0.000         0.531
 Y4             0.000         0.000         0.000         0.257


           ALPHA
              I             S
              ________      ________
      1         1.417         2.011


           BETA
              I             S
              ________      ________
 I              0.000         0.000
 S              0.000         0.000


           PSI
              I             S
              ________      ________
 I              1.101
 S              0.226         0.261


     Beginning Time:  14:34:08
        Ending Time:  14:34:08
       Elapsed Time:  00:00:00



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