Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:12 PM
INPUT INSTRUCTIONS
TITLE: this is an example of an N=1 time series analysis
with a bivariate cross-lagged model with two factors
and continuous factor indicators
DATA: FILE = ex6.28.dat;
VARIABLE: NAMES = y11-y14 y21-y24;
ANALYSIS: ESTIMATOR = BAYES;
PROCESSORS = 2;
BITERATIONS = (2000);
MODEL: f1 BY y11-y14 (&1);
f2 BY y21-y24 (&1);
f1 ON f1&1 f2&1;
f2 ON f2&1 f1&1;
OUTPUT: TECH1 TECH8;
PLOT: TYPE = PLOT3;
INPUT READING TERMINATED NORMALLY
this is an example of an N=1 time series analysis
with a bivariate cross-lagged model with two factors
and continuous factor indicators
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 100
Number of dependent variables 8
Number of independent variables 0
Number of continuous latent variables 4
Observed dependent variables
Continuous
Y11 Y12 Y13 Y14 Y21 Y22
Y23 Y24
Continuous latent variables
F1 F2 F1&1 F2&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)
ex6.28.dat
Input data format FREE
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
Y11 0.021 -0.018 -3.685 1.00% -1.103 -0.413 -0.197
100.000 2.272 -0.225 3.860 1.00% 0.344 1.352
Y12 0.160 -0.169 -3.611 1.00% -1.121 -0.272 0.302
100.000 2.334 -0.321 3.829 1.00% 0.492 1.579
Y13 0.136 0.051 -3.730 1.00% -1.264 -0.420 -0.011
100.000 2.898 -0.349 4.298 1.00% 0.437 1.520
Y14 0.105 -0.019 -3.766 1.00% -1.444 -0.267 0.256
100.000 3.022 -0.488 3.956 1.00% 0.525 1.467
Y21 -0.081 0.008 -3.022 1.00% -1.435 -0.527 -0.075
100.000 2.129 -0.700 3.141 1.00% 0.262 1.205
Y22 -0.144 -0.417 -4.586 1.00% -1.240 -0.450 -0.082
100.000 2.368 0.006 3.045 1.00% 0.270 1.079
Y23 0.055 -0.050 -3.759 1.00% -1.365 -0.256 0.058
100.000 2.595 -0.417 3.925 1.00% 0.317 1.321
Y24 -0.133 -0.109 -3.628 1.00% -1.648 -0.335 0.076
100.000 2.233 -0.638 3.133 1.00% 0.350 1.064
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 29
Information Criteria
Deviance (DIC) 2403.245
Estimated Number of Parameters (pD) 168.227
MODEL RESULTS
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
F1 BY
Y11 1.000 0.000 0.000 1.000 1.000
Y12 1.070 0.135 0.000 0.849 1.390 *
Y13 1.199 0.148 0.000 0.945 1.543 *
Y14 1.163 0.151 0.000 0.898 1.496 *
F2 BY
Y21 1.000 0.000 0.000 1.000 1.000
Y22 1.118 0.162 0.000 0.858 1.508 *
Y23 1.090 0.167 0.000 0.829 1.469 *
Y24 0.900 0.146 0.000 0.659 1.232 *
F1 ON
F1&1 0.441 0.128 0.001 0.177 0.683 *
F2&1 0.197 0.137 0.060 -0.047 0.489
F2 ON
F2&1 0.414 0.146 0.002 0.133 0.707 *
F1&1 0.141 0.140 0.162 -0.142 0.398
F2 WITH
F1 0.491 0.159 0.000 0.238 0.874 *
Intercepts
Y11 -0.054 0.252 0.405 -0.633 0.396
Y12 0.072 0.263 0.393 -0.505 0.569
Y13 0.042 0.296 0.435 -0.612 0.567
Y14 0.013 0.294 0.481 -0.622 0.533
Y21 -0.168 0.228 0.228 -0.604 0.292
Y22 -0.238 0.250 0.181 -0.739 0.252
Y23 -0.036 0.254 0.442 -0.537 0.441
Y24 -0.215 0.216 0.167 -0.639 0.217
Residual Variances
Y11 0.910 0.166 0.000 0.650 1.308 *
Y12 0.789 0.160 0.000 0.519 1.148 *
Y13 0.980 0.189 0.000 0.667 1.411 *
Y14 1.226 0.233 0.000 0.843 1.735 *
Y21 0.842 0.174 0.000 0.562 1.233 *
Y22 0.807 0.187 0.000 0.502 1.207 *
Y23 1.137 0.219 0.000 0.783 1.650 *
Y24 1.260 0.218 0.000 0.911 1.755 *
F1 1.009 0.248 0.000 0.627 1.609 *
F2 1.039 0.275 0.000 0.596 1.658 *
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION
NU
Y11 Y12 Y13 Y14 Y21
________ ________ ________ ________ ________
1 2 3 4 5
NU
Y22 Y23 Y24
________ ________ ________
6 7 8
LAMBDA
F1 F2 F1&1 F2&1
________ ________ ________ ________
Y11 0 0 0 0
Y12 9 0 0 0
Y13 10 0 0 0
Y14 11 0 0 0
Y21 0 0 0 0
Y22 0 12 0 0
Y23 0 13 0 0
Y24 0 14 0 0
THETA
Y11 Y12 Y13 Y14 Y21
________ ________ ________ ________ ________
Y11 15
Y12 0 16
Y13 0 0 17
Y14 0 0 0 18
Y21 0 0 0 0 19
Y22 0 0 0 0 0
Y23 0 0 0 0 0
Y24 0 0 0 0 0
THETA
Y22 Y23 Y24
________ ________ ________
Y22 20
Y23 0 21
Y24 0 0 22
ALPHA
F1 F2 F1&1 F2&1
________ ________ ________ ________
0 0 0 0
BETA
F1 F2 F1&1 F2&1
________ ________ ________ ________
F1 0 0 23 24
F2 0 0 25 26
F1&1 0 0 0 0
F2&1 0 0 0 0
PSI
F1 F2 F1&1 F2&1
________ ________ ________ ________
F1 27
F2 28 29
F1&1 0 0 0
F2&1 0 0 0 0
STARTING VALUES
NU
Y11 Y12 Y13 Y14 Y21
________ ________ ________ ________ ________
0.021 0.160 0.136 0.105 -0.081
NU
Y22 Y23 Y24
________ ________ ________
-0.144 0.055 -0.133
LAMBDA
F1 F2 F1&1 F2&1
________ ________ ________ ________
Y11 1.000 0.000 0.000 0.000
Y12 1.000 0.000 0.000 0.000
Y13 1.000 0.000 0.000 0.000
Y14 1.000 0.000 0.000 0.000
Y21 0.000 1.000 0.000 0.000
Y22 0.000 1.000 0.000 0.000
Y23 0.000 1.000 0.000 0.000
Y24 0.000 1.000 0.000 0.000
THETA
Y11 Y12 Y13 Y14 Y21
________ ________ ________ ________ ________
Y11 1.136
Y12 0.000 1.167
Y13 0.000 0.000 1.449
Y14 0.000 0.000 0.000 1.511
Y21 0.000 0.000 0.000 0.000 1.065
Y22 0.000 0.000 0.000 0.000 0.000
Y23 0.000 0.000 0.000 0.000 0.000
Y24 0.000 0.000 0.000 0.000 0.000
THETA
Y22 Y23 Y24
________ ________ ________
Y22 1.184
Y23 0.000 1.298
Y24 0.000 0.000 1.116
ALPHA
F1 F2 F1&1 F2&1
________ ________ ________ ________
0.000 0.000 0.000 0.000
BETA
F1 F2 F1&1 F2&1
________ ________ ________ ________
F1 0.000 0.000 0.000 0.000
F2 0.000 0.000 0.000 0.000
F1&1 0.000 0.000 0.000 0.000
F2&1 0.000 0.000 0.000 0.000
PSI
F1 F2 F1&1 F2&1
________ ________ ________ ________
F1 1.000
F2 0.000 1.000
F1&1 0.000 0.000 1.000
F2&1 0.000 0.000 0.000 1.000
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~N(0.000,infinity) 0.0000 infinity infinity
Parameter 8~N(0.000,infinity) 0.0000 infinity infinity
Parameter 9~N(0.000,infinity) 0.0000 infinity infinity
Parameter 10~N(0.000,infinity) 0.0000 infinity infinity
Parameter 11~N(0.000,infinity) 0.0000 infinity infinity
Parameter 12~N(0.000,infinity) 0.0000 infinity infinity
Parameter 13~N(0.000,infinity) 0.0000 infinity infinity
Parameter 14~N(0.000,infinity) 0.0000 infinity infinity
Parameter 15~IG(-1.000,0.000) infinity infinity infinity
Parameter 16~IG(-1.000,0.000) infinity infinity infinity
Parameter 17~IG(-1.000,0.000) infinity infinity infinity
Parameter 18~IG(-1.000,0.000) infinity infinity infinity
Parameter 19~IG(-1.000,0.000) infinity infinity infinity
Parameter 20~IG(-1.000,0.000) infinity infinity infinity
Parameter 21~IG(-1.000,0.000) infinity infinity infinity
Parameter 22~IG(-1.000,0.000) infinity infinity infinity
Parameter 23~N(0.000,infinity) 0.0000 infinity infinity
Parameter 24~N(0.000,infinity) 0.0000 infinity infinity
Parameter 25~N(0.000,infinity) 0.0000 infinity infinity
Parameter 26~N(0.000,infinity) 0.0000 infinity infinity
Parameter 27~IW(0.000,-3) infinity infinity infinity
Parameter 28~IW(0.000,-3) infinity infinity infinity
Parameter 29~IW(0.000,-3) 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 1.190 4
200 1.081 9
300 1.468 6
400 1.348 1
500 1.134 1
600 1.075 5
700 1.034 23
800 1.031 29
900 1.054 1
1000 1.030 7
1100 1.101 3
1200 1.099 3
1300 1.070 1
1400 1.076 1
1500 1.054 2
1600 1.012 20
1700 1.011 28
1800 1.009 20
1900 1.016 28
2000 1.021 20
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
Beginning Time: 23:12:31
Ending Time: 23:12:31
Elapsed Time: 00:00:00
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