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

INPUT INSTRUCTIONS

  TITLE:	this is an example of a QTL sibling model
  for a continuous outcome using parameter constraints
  DATA:	FILE = ex5.23.dat;
  VARIABLE:	NAMES = y1 y2 pihat;
  	USEVARIABLES = y1 y2;
  	CONSTRAINT = pihat;
  MODEL:	[y1-y2] (1);
  	y1-y2 (var);
  	y1 WITH y2 (cov);
  MODEL CONSTRAINT:
  	NEW(a e q);
  	var = a**2 + e**2 + q**2;
  	cov = 0.5*a**2 + pihat*q**2;



INPUT READING TERMINATED NORMALLY



this is an example of a QTL sibling model
for a continuous outcome using parameter constraints

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        2000

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

Observed dependent variables

  Continuous
   Y1          Y2


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)
  ex5.23.dat

Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        4

Loglikelihood

          H0 Value                       -6355.741

Information Criteria

          Akaike (AIC)                   12719.482
          Bayesian (BIC)                 12741.885
          Sample-Size Adjusted BIC       12729.177
            (n* = (n + 2) / 24)



MODEL RESULTS

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

 Y1       WITH
    Y2               999.000      0.000    999.000    999.000

 Means
    Y1                 0.012      0.023      0.546      0.585
    Y2                 0.012      0.023      0.546      0.585

 Variances
    Y1                 1.528      0.037     41.774      0.000
    Y2                 1.528      0.037     41.774      0.000

New/Additional Parameters
    A                  0.848      0.064     13.218      0.000
    E                  0.622      0.045     13.822      0.000
    Q                  0.649      0.059     11.078      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.188E-03
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  17:48:37
        Ending Time:  17:48:37
       Elapsed Time:  00:00:00



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