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

INPUT INSTRUCTIONS

  TITLE:	this is an example of a Bayesian MIMIC model with cross-loadings and direct effects
  DATA:	FILE = ex5.32.dat;
  VARIABLE:	NAMES = y1-y6 x1-x3;
  ANALYSIS:	ESTIMATOR = BAYES;
  	PROCESSORS = 2;
  MODEL:	f1 BY y1-y3
  	y4-y6 (xload4-xload6);
  	f2 BY y4-y6
  	y1-y3 (xload1-xload3);
  	f1-f2 ON x1-x3;
  	y1-y6 ON x1-x3 (dir1-dir18);
  MODEL PRIORS:
  	xload1-xload6~N(0,0.01);
  	dir1-dir18~N(0,0.01);
  PLOT:	TYPE = PLOT2;	

  	



INPUT READING TERMINATED NORMALLY



this is an example of a Bayesian MIMIC model with cross-loadings and direct effects

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    6
Number of independent variables                                  3
Number of continuous latent variables                            2

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4          Y5          Y6

Observed independent variables
   X1          X2          X3

Continuous latent variables
   F1          F2


Estimator                                                    BAYES
Specifications for Bayesian Estimation
  Point estimate                                            MEDIAN
  Number of Markov chain Monte Carlo (MCMC) chains               2
  Random seed for the first chain                                0
  Starting value information                           UNPERTURBED
  Treatment of categorical mediator                         LATENT
  Algorithm used for Markov chain Monte Carlo           GIBBS(PX1)
  Convergence criterion                                  0.500D-01
  Maximum number of iterations                               50000
  K-th iteration used for thinning                               1

Input data file(s)
  ex5.32.dat
Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY

     USE THE FBITERATIONS OPTION TO INCREASE THE NUMBER OF ITERATIONS BY A FACTOR
     OF AT LEAST TWO TO CHECK CONVERGENCE AND THAT THE PSR VALUE DOES NOT INCREASE.



MODEL FIT INFORMATION

Number of Free Parameters                              49

Bayesian Posterior Predictive Checking using Chi-Square

          95% Confidence Interval for the Difference Between
          the Observed and the Replicated Chi-Square Values

                                -26.748            30.445

          Posterior Predictive P-Value              0.572

Information Criterion

          Deviance (DIC)                         8064.188
          Estimated Number of Parameters (pD)      38.654
          Bayesian (BIC)                         8291.205



MODEL RESULTS

                                Posterior  One-Tailed         95% C.I.
                    Estimate       S.D.      P-Value   Lower 2.5%  Upper 2.5%  Significance

 F1       BY
    Y1                 1.000       0.000      0.000       1.000       1.000
    Y2                 1.040       0.072      0.000       0.912       1.188      *
    Y3                 1.001       0.069      0.000       0.877       1.143      *
    Y4                 0.065       0.065      0.154      -0.055       0.193
    Y5                -0.049       0.072      0.269      -0.181       0.100
    Y6                -0.008       0.063      0.444      -0.126       0.121

 F2       BY
    Y4                 1.000       0.000      0.000       1.000       1.000
    Y5                 1.077       0.078      0.000       0.938       1.227      *
    Y6                 0.945       0.071      0.000       0.811       1.085      *
    Y1                -0.009       0.072      0.439      -0.158       0.122
    Y2                -0.002       0.074      0.487      -0.147       0.134
    Y3                 0.037       0.072      0.298      -0.111       0.176

 F1         ON
    X1                 0.483       0.079      0.000       0.327       0.635      *
    X2                 0.557       0.069      0.000       0.438       0.699      *
    X3                 0.706       0.072      0.000       0.583       0.903      *

 F2         ON
    X1                 0.646       0.072      0.000       0.480       0.773      *
    X2                 0.573       0.063      0.000       0.445       0.691      *
    X3                 0.433       0.062      0.000       0.308       0.552      *

 Y1         ON
    X1                 0.012       0.064      0.429      -0.105       0.141
    X2                 0.019       0.057      0.363      -0.088       0.130
    X3                 0.000       0.062      0.497      -0.128       0.118

 Y2         ON
    X1                 0.005       0.065      0.474      -0.121       0.132
    X2                 0.013       0.060      0.415      -0.106       0.124
    X3                -0.033       0.063      0.297      -0.164       0.086

 Y3         ON
    X1                 0.006       0.062      0.460      -0.113       0.127
    X2                -0.016       0.060      0.409      -0.134       0.097
    X3                -0.005       0.062      0.471      -0.129       0.111

 Y4         ON
    X1                 0.017       0.064      0.403      -0.101       0.156
    X2                -0.030       0.059      0.301      -0.142       0.091
    X3                -0.029       0.057      0.313      -0.146       0.072

 Y5         ON
    X1                -0.009       0.065      0.451      -0.123       0.127
    X2                -0.014       0.065      0.418      -0.136       0.111
    X3                -0.016       0.060      0.397      -0.131       0.101

 Y6         ON
    X1                 0.053       0.062      0.193      -0.065       0.176
    X2                 0.085       0.061      0.085      -0.032       0.200
    X3                 0.018       0.059      0.375      -0.097       0.136

 F2       WITH
    F1                 0.224       0.080      0.005       0.068       0.379      *

 Intercepts
    Y1                 0.037       0.110      0.383      -0.173       0.257
    Y2                 0.035       0.112      0.369      -0.184       0.258
    Y3                 0.031       0.111      0.403      -0.173       0.243
    Y4                 0.166       0.113      0.067      -0.046       0.397
    Y5                 0.194       0.102      0.024       0.001       0.396      *
    Y6                 0.117       0.107      0.147      -0.087       0.324

 Residual Variances
    Y1                 0.536       0.051      0.000       0.436       0.634      *
    Y2                 0.516       0.053      0.000       0.419       0.629      *
    Y3                 0.496       0.048      0.000       0.407       0.594      *
    Y4                 0.560       0.050      0.000       0.466       0.661      *
    Y5                 0.360       0.047      0.000       0.275       0.461      *
    Y6                 0.551       0.048      0.000       0.463       0.653      *
    F1                 0.735       0.091      0.000       0.583       0.935      *
    F2                 0.624       0.079      0.000       0.484       0.794      *


PLOT INFORMATION

The following plots are available:

  Bayesian posterior parameter distributions
  Bayesian posterior parameter trace plots
  Bayesian autocorrelation plots
  Bayesian prior parameter distributions
  Bayesian posterior predictive checking scatterplots
  Bayesian posterior predictive checking distribution plots

     Beginning Time:  22:55:34
        Ending Time:  22:55:34
       Elapsed Time:  00:00:00



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