Mplus VERSION 7.2
MUTHEN & MUTHEN
05/07/2014   2:40 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a linear growth model
  	for a continuous outcome with first-order auto correlated
  	residuals using non-linear constraints
  DATA:	FILE = ex6.17.dat;
  VARIABLE:	NAMES = y1-y4;
  MODEL:	i s | y1@0 y2@1 y3@2 y4@3;
  	y1-y4 (resvar);
  	y1-y3 PWITH y2-y4 (p1);
  	y1-y2 PWITH y3-y4 (p2);
  	y1 WITH y4 (p3);
  MODEL CONSTRAINT:
  	NEW (corr);
  	p1 = resvar*corr;
  	p2 = resvar*corr**2;
  	p3 = resvar*corr**3;



INPUT READING TERMINATED NORMALLY



this is an example of a linear growth model
for a continuous outcome with first-order auto correlated
residuals using non-linear constraints

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        1000

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

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4

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

Input data file(s)
  ex6.17.dat

Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        7

Loglikelihood

          H0 Value                       -6593.460
          H1 Value                       -6592.226

Information Criteria

          Akaike (AIC)                   13200.921
          Bayesian (BIC)                 13235.275
          Sample-Size Adjusted BIC       13213.043
            (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

          Value                              2.469
          Degrees of Freedom                     7
          P-Value                           0.9294

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.000
          90 Percent C.I.                    0.000  0.012
          Probability RMSEA <= .05           1.000

CFI/TLI

          CFI                                1.000
          TLI                                1.002

Chi-Square Test of Model Fit for the Baseline Model

          Value                           2118.221
          Degrees of Freedom                     6
          P-Value                           0.0000

SRMR (Standardized Root Mean Square Residual)

          Value                              0.006



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                  0.139      0.052      2.680      0.007

 Y1       WITH
    Y4                 0.185      0.102      1.820      0.069
    Y2                 0.734      0.209      3.507      0.000
    Y3                 0.368      0.154      2.397      0.017

 Y2       WITH
    Y3                 0.734      0.209      3.507      0.000
    Y4                 0.368      0.154      2.397      0.017

 Y3       WITH
    Y4                 0.734      0.209      3.507      0.000

 Means
    I                  0.011      0.042      0.266      0.790
    S                  1.011      0.021     49.078      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                  0.424      0.253      1.676      0.094
    S                  0.142      0.034      4.135      0.000

 Residual Variances
    Y1                 1.462      0.225      6.490      0.000
    Y2                 1.462      0.225      6.490      0.000
    Y3                 1.462      0.225      6.490      0.000
    Y4                 1.462      0.225      6.490      0.000

New/Additional Parameters
    CORR               0.502      0.067      7.543      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.159E-03
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  14:40:53
        Ending Time:  14:40:53
       Elapsed Time:  00:00:00



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