Mplus VERSION 7.4 DEMO MUTHEN & MUTHEN 02/26/2017 3:06 PM INPUT INSTRUCTIONS DATA: FILE IS data_example.dat; VARIABLE: NAMES ARE months lag age age_sq; USEVARIABLES ARE lag age age_sq; ANALYSIS: ESTIMATOR IS Bayes; MODEL: [lag] (a); lag on age (b); lag on age_sq (c); lag (d); MODEL PRIORS: a ~ N(10,5); b ~ N(.8, 5); c ~ N(0, 10); d ~ IG(0.5, 0.5); OUTPUT: STAND tech8; PLOT: TYPE IS PLOT2; INPUT READING TERMINATED NORMALLY SUMMARY OF ANALYSIS Number of groups 1 Number of observations 304 Number of dependent variables 1 Number of independent variables 2 Number of continuous latent variables 0 Observed dependent variables Continuous LAG Observed independent variables AGE AGE_SQ 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) data_example.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 4 Bayesian Posterior Predictive Checking using Chi-Square 95% Confidence Interval for the Difference Between the Observed and the Replicated Chi-Square Values -7.764 4.675 Posterior Predictive P-Value 0.500 Information Criteria Deviance (DIC) 2466.485 Estimated Number of Parameters (pD) 3.281 Bayesian (BIC) 2482.778 MODEL RESULTS Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance LAG ON AGE 1.410 0.240 0.000 1.017 1.959 * AGE_SQ -0.046 0.010 0.000 -0.065 -0.026 * Intercepts LAG 10.646 0.705 0.000 9.350 12.111 * Residual Variances LAG 194.743 16.581 0.000 162.464 226.894 * STANDARDIZED MODEL RESULTS STDYX Standardization Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance LAG ON AGE 0.438 0.069 0.000 0.313 0.609 * AGE_SQ -0.353 0.073 0.000 -0.497 -0.199 * Intercepts LAG 0.733 0.057 0.000 0.631 0.857 * Residual Variances LAG 0.924 0.023 0.000 0.858 0.961 * STDY Standardization Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance LAG ON AGE 0.098 0.015 0.000 0.070 0.136 * AGE_SQ -0.003 0.001 0.000 -0.004 -0.002 * Intercepts LAG 0.733 0.057 0.000 0.631 0.857 * Residual Variances LAG 0.924 0.023 0.000 0.858 0.961 * STD Standardization Posterior One-Tailed 95% C.I. Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance LAG ON AGE 1.410 0.240 0.000 1.017 1.959 * AGE_SQ -0.046 0.010 0.000 -0.065 -0.026 * Intercepts LAG 10.646 0.705 0.000 9.350 12.111 * Residual Variances LAG 194.743 16.581 0.000 162.464 226.894 * R-SQUARE Posterior One-Tailed 95% C.I. Variable Estimate S.D. P-Value Lower 2.5% Upper 2.5% LAG 0.076 0.023 0.000 0.039 0.142 TECHNICAL 1 OUTPUT PARAMETER SPECIFICATION NU LAG AGE AGE_SQ ________ ________ ________ 1 0 0 0 LAMBDA LAG AGE AGE_SQ ________ ________ ________ LAG 0 0 0 AGE 0 0 0 AGE_SQ 0 0 0 THETA LAG AGE AGE_SQ ________ ________ ________ LAG 0 AGE 0 0 AGE_SQ 0 0 0 ALPHA LAG AGE AGE_SQ ________ ________ ________ 1 1 0 0 BETA LAG AGE AGE_SQ ________ ________ ________ LAG 0 2 3 AGE 0 0 0 AGE_SQ 0 0 0 PSI LAG AGE AGE_SQ ________ ________ ________ LAG 4 AGE 0 0 AGE_SQ 0 0 0 STARTING VALUES NU LAG AGE AGE_SQ ________ ________ ________ 1 0.000 0.000 0.000 LAMBDA LAG AGE AGE_SQ ________ ________ ________ LAG 1.000 0.000 0.000 AGE 0.000 1.000 0.000 AGE_SQ 0.000 0.000 1.000 THETA LAG AGE AGE_SQ ________ ________ ________ LAG 0.000 AGE 0.000 0.000 AGE_SQ 0.000 0.000 0.000 ALPHA LAG AGE AGE_SQ ________ ________ ________ 1 9.628 0.000 0.000 BETA LAG AGE AGE_SQ ________ ________ ________ LAG 0.000 0.000 0.000 AGE 0.000 0.000 0.000 AGE_SQ 0.000 0.000 0.000 PSI LAG AGE AGE_SQ ________ ________ ________ LAG 103.528 AGE 0.000 10.007 AGE_SQ 0.000 0.000 6166.732 PRIORS FOR ALL PARAMETERS PRIOR MEAN PRIOR VARIANCE PRIOR STD. DEV. Parameter 1~N(10.000,5.000) 10.0000 5.0000 2.2361 Parameter 2~N(0.800,5.000) 0.8000 5.0000 2.2361 Parameter 3~N(0.000,10.000) 0.0000 10.0000 3.1623 Parameter 4~IG(0.500,0.500) infinity infinity infinity TECHNICAL 8 OUTPUT Kolmogorov-Smirnov comparing posterior distributions across chains 1 and 2 using 100 draws. Parameter KS Statistic P-value Parameter 4 0.1800 0.3584 Parameter 2 0.1400 0.6779 Parameter 1 0.1200 0.8409 Parameter 3 0.0000 1.0000 Simulated prior distributions Parameter Prior Mean Prior Variance Prior Std. Dev. Parameter 1 10.0695 5.5116 2.3477 Parameter 2 0.8555 4.9317 2.2207 Parameter 3 -0.1698 10.0397 3.1686 Parameter 4 1796.3699 2421304823.8905 49206.7559 TECHNICAL 8 OUTPUT FOR BAYES ESTIMATION CHAIN BSEED 1 0 2 285380 POTENTIAL PARAMETER WITH ITERATION SCALE REDUCTION HIGHEST PSR 100 1.007 3 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 DIAGRAM INFORMATION Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram. If running Mplus from the Mplus Diagrammer, the diagram opens automatically. Diagram output c:\users\naomi\dropbox\exercises - naomi\bayes - 2\1. mplus\data and input files\model_1.dgm Beginning Time: 15:06:52 Ending Time: 15:06:52 Elapsed Time: 00:00:00 Mplus VERSION 7.4 DEMO has the following limitations: Maximum number of dependent variables: 6 Maximum number of independent variables: 2 Maximum number of between variables: 2 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) 1998-2015 Muthen & Muthen