Mplus VERSION 8.8
MUTHEN & MUTHEN
11/22/2022 8:57 AM
OUTPUT SECTIONS
INPUT INSTRUCTIONS
TITLE: N=1 bivariate DSEM model
DATA:
FILE = Nis1data.dat;
VARIABLE:
NAMES = y x;
LAGGED = y(1) x(1);
MISSING = ALL(-999);
ANALYSIS:
ESTIMATOR = BAYES;
BITER = (1000);
PROC = 2;
MODEL:
y ON y&1 x;
x ON y&1 x&1;
OUTPUT:
TECH1 TECH8 STDYX;
PLOT:
TYPE = PLOT3;
INPUT READING TERMINATED NORMALLY
N=1 bivariate DSEM model
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 100
Number of dependent variables 2
Number of independent variables 2
Number of continuous latent variables 0
Observed dependent variables
Continuous
Y X
Observed independent variables
Y&1 X&1
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
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)
Nis1data.dat
Input data format FREE
SUMMARY OF DATA
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
Number of missing data patterns 4
PROPORTION OF DATA PRESENT
Covariance Coverage
Y X
________ ________
Y 0.890
X 0.890 0.890
UNIVARIATE SAMPLE STATISTICS
UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
Y 5.856 -0.316 1.910 1.12% 4.950 5.580 5.730
89.000 1.214 0.773 8.430 1.12% 6.050 6.740
X 13.122 0.425 9.570 1.12% 12.000 12.840 13.110
89.000 1.972 0.631 17.300 1.12% 13.280 14.220
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 8
Information Criteria
Deviance (DIC) 646.679
Estimated Number of Parameters (pD) 27.903
MODEL RESULTS
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
Y ON
Y&1 0.284 0.102 0.005 0.082 0.486 *
X -0.235 0.081 0.002 -0.399 -0.068 *
X ON
Y&1 -0.117 0.151 0.235 -0.414 0.189
X&1 0.373 0.116 0.000 0.163 0.620 *
Intercepts
Y 7.340 1.359 0.000 4.573 10.064 *
X 8.846 2.062 0.000 4.563 12.674 *
Residual Variances
Y 1.006 0.161 0.000 0.737 1.379 *
X 1.743 0.270 0.000 1.299 2.342 *
STANDARDIZED MODEL RESULTS
STDYX Standardization
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
Y ON
Y&1 0.284 0.102 0.005 0.082 0.486 *
X -0.307 0.100 0.002 -0.487 -0.088 *
X ON
Y&1 -0.092 0.116 0.235 -0.310 0.139
X&1 0.373 0.116 0.000 0.163 0.620 *
Intercepts
Y 6.417 1.212 0.000 3.895 8.603 *
X 6.069 1.630 0.000 2.731 9.138 *
Residual Variances
Y 0.779 0.087 0.000 0.580 0.921 *
X 0.811 0.092 0.000 0.586 0.950 *
R-SQUARE
Posterior One-Tailed 95% C.I.
Variable Estimate S.D. P-Value Lower 2.5% Upper 2.5%
Y 0.221 0.087 0.000 0.078 0.418
X 0.189 0.092 0.000 0.050 0.414
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION
NU
Y X Y&1 X&1
________ ________ ________ ________
0 0 0 0
LAMBDA
Y X Y&1 X&1
________ ________ ________ ________
Y 0 0 0 0
X 0 0 0 0
Y&1 0 0 0 0
X&1 0 0 0 0
THETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0
X 0 0
Y&1 0 0 0
X&1 0 0 0 0
ALPHA
Y X Y&1 X&1
________ ________ ________ ________
1 2 0 0
BETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0 3 4 0
X 0 0 5 6
Y&1 0 0 0 0
X&1 0 0 0 0
PSI
Y X Y&1 X&1
________ ________ ________ ________
Y 7
X 0 8
Y&1 0 0 0
X&1 0 0 0 0
STARTING VALUES
NU
Y X Y&1 X&1
________ ________ ________ ________
0.000 0.000 0.000 0.000
LAMBDA
Y X Y&1 X&1
________ ________ ________ ________
Y 1.000 0.000 0.000 0.000
X 0.000 1.000 0.000 0.000
Y&1 0.000 0.000 1.000 0.000
X&1 0.000 0.000 0.000 1.000
THETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0.000
X 0.000 0.000
Y&1 0.000 0.000 0.000
X&1 0.000 0.000 0.000 0.000
ALPHA
Y X Y&1 X&1
________ ________ ________ ________
5.856 13.122 0.000 0.000
BETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0.000 0.000 0.000 0.000
X 0.000 0.000 0.000 0.000
Y&1 0.000 0.000 0.000 0.000
X&1 0.000 0.000 0.000 0.000
PSI
Y X Y&1 X&1
________ ________ ________ ________
Y 0.607
X 0.000 0.986
Y&1 0.000 0.000 0.600
X&1 0.000 0.000 0.000 0.975
PRIORS FOR ALL PARAMETERS PRIOR MEAN PRIOR VARIANCE PRIOR STD. DEV.
Parameter 1~N(0.000,infinity) 0.0000 infinity infinity
Parameter 2~N(0.000,infinity) 0.0000 infinity infinity
Parameter 3~N(0.000,infinity) 0.0000 infinity infinity
Parameter 4~N(0.000,infinity) 0.0000 infinity infinity
Parameter 5~N(0.000,infinity) 0.0000 infinity infinity
Parameter 6~N(0.000,infinity) 0.0000 infinity infinity
Parameter 7~IG(-1.000,0.000) infinity infinity infinity
Parameter 8~IG(-1.000,0.000) infinity infinity infinity
TECHNICAL 8 OUTPUT
TECHNICAL 8 OUTPUT FOR BAYES ESTIMATION
CHAIN BSEED
1 0
2 285380
POTENTIAL PARAMETER WITH
ITERATION SCALE REDUCTION HIGHEST PSR
100 1.012 5
200 1.017 7
300 1.010 3
400 1.003 6
500 1.001 3
600 1.003 4
700 1.002 4
800 1.005 5
900 1.001 1
1000 1.003 1
PLOT INFORMATION
The following plots are available:
Histograms (sample values)
Scatterplots (sample values)
Time series plots (sample values, ACF, PACF)
Bayesian posterior parameter distributions
Bayesian posterior parameter trace plots
Bayesian autocorrelation 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:\simulations\nis1model.dgm
Beginning Time: 08:57:31
Ending Time: 08:57:31
Elapsed Time: 00:00:00
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