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

INPUT INSTRUCTIONS

  TITLE:	this is an example of a Bayesian bi-factor CFA with two items
          loading on only the general factor and cross-loadings with
          zero-mean and small-variance priors
  DATA:	FILE = ex5.31.dat;
  VARIABLE:	NAMES = y1-y10;
  ANALYSIS:	ESTIMATOR = BAYES;
  	PROCESSORS = 2;
  MODEL:	fg BY y1-y10*;
  	fg@1;
  	f1 BY y1-y4
  	y5-y10 (f1xlam5-f1xlam10);
  	f2 BY y5-y8
  	y1-y4 y9-y10(f2xlam1-f2xlam6);
  	fg WITH f1-f2@0;
  MODEL PRIORS:
  	f1xlam5-f2xlam6~N(0,0.01);
  PLOT:	TYPE = PLOT2;






INPUT READING TERMINATED NORMALLY



this is an example of a Bayesian bi-factor CFA with two items
loading on only the general factor and cross-loadings with
zero-mean and small-variance priors

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                   10
Number of independent variables                                  0
Number of continuous latent variables                            3

Observed dependent variables

  Continuous
   Y1          Y2          Y3          Y4          Y5          Y6
   Y7          Y8          Y9          Y10

Continuous latent variables
   FG          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)
  ex5.31.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                              51

Bayesian Posterior Predictive Checking using Chi-Square

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

                                -31.351            35.584

          Posterior Predictive P-Value              0.387

Information Criterion

          Deviance (DIC)                        11569.991
          Estimated Number of Parameters (pD)      40.498
          Bayesian (BIC)                        11804.965



MODEL RESULTS

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

 FG       BY
    Y1                 0.683       0.045      0.000       0.597       0.774      *
    Y2                 0.728       0.048      0.000       0.636       0.817      *
    Y3                 0.744       0.045      0.000       0.661       0.834      *
    Y4                 0.742       0.048      0.000       0.652       0.841      *
    Y5                 0.753       0.045      0.000       0.664       0.840      *
    Y6                 0.705       0.045      0.000       0.619       0.795      *
    Y7                 0.671       0.049      0.000       0.571       0.769      *
    Y8                 0.719       0.050      0.000       0.620       0.816      *
    Y9                 0.708       0.044      0.000       0.627       0.794      *
    Y10                0.628       0.047      0.000       0.541       0.725      *

 F1       BY
    Y1                 1.000       0.000      0.000       1.000       1.000
    Y2                 0.965       0.157      0.000       0.689       1.325      *
    Y3                 0.765       0.132      0.000       0.513       1.015      *
    Y4                 0.847       0.155      0.000       0.525       1.161      *
    Y5                -0.012       0.080      0.434      -0.169       0.152
    Y6                -0.071       0.078      0.166      -0.226       0.082
    Y7                 0.088       0.083      0.141      -0.076       0.254
    Y8                -0.014       0.081      0.427      -0.174       0.146
    Y9                -0.003       0.091      0.487      -0.190       0.175
    Y10                0.034       0.090      0.346      -0.159       0.206

 F2       BY
    Y5                 1.000       0.000      0.000       1.000       1.000
    Y6                 0.957       0.299      0.000       0.558       1.714      *
    Y7                 1.106       0.574      0.000       0.597       2.858      *
    Y8                 1.162       0.372      0.000       0.715       2.134      *
    Y1                 0.017       0.088      0.426      -0.153       0.187
    Y2                 0.017       0.089      0.430      -0.154       0.197
    Y3                -0.070       0.088      0.208      -0.251       0.089
    Y4                 0.024       0.083      0.389      -0.154       0.179
    Y9                 0.028       0.095      0.393      -0.162       0.218
    Y10               -0.023       0.089      0.385      -0.197       0.150

 FG       WITH
    F1                 0.000       0.000      1.000       0.000       0.000
    F2                 0.000       0.000      1.000       0.000       0.000

 F2       WITH
    F1                 0.024       0.037      0.180      -0.044       0.108

 Intercepts
    Y1                -0.075       0.049      0.054      -0.175       0.016
    Y2                 0.045       0.049      0.179      -0.050       0.139
    Y3                 0.010       0.048      0.409      -0.084       0.100
    Y4                -0.044       0.050      0.173      -0.140       0.055
    Y5                 0.022       0.049      0.341      -0.077       0.110
    Y6                -0.025       0.048      0.298      -0.121       0.063
    Y7                 0.022       0.049      0.326      -0.076       0.113
    Y8                 0.060       0.051      0.116      -0.047       0.165
    Y9                -0.019       0.047      0.359      -0.113       0.070
    Y10               -0.001       0.047      0.487      -0.097       0.089

 Variances
    FG                 1.000       0.000      0.000       1.000       1.000
    F1                 0.211       0.054      0.000       0.114       0.322      *
    F2                 0.109       0.055      0.000       0.028       0.227      *

 Residual Variances
    Y1                 0.328       0.032      0.000       0.260       0.389      *
    Y2                 0.320       0.030      0.000       0.260       0.378      *
    Y3                 0.303       0.026      0.000       0.258       0.358      *
    Y4                 0.370       0.029      0.000       0.317       0.430      *
    Y5                 0.387       0.033      0.000       0.324       0.455      *
    Y6                 0.397       0.034      0.000       0.336       0.465      *
    Y7                 0.440       0.048      0.000       0.334       0.525      *
    Y8                 0.494       0.042      0.000       0.418       0.583      *
    Y9                 0.506       0.044      0.000       0.416       0.591      *
    Y10                0.576       0.045      0.000       0.492       0.670      *


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:  14:40:42
        Ending Time:  14:40:42
       Elapsed Time:  00:00:00



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