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 for continuous outcomes
DATA: FILE = ex6.25.dat;
VARIABLE: NAMES = y1 y2;
LAGGED = y1(1) y2(1);
ANALYSIS: ESTIMATOR = BAYES;
PROCESSORS = 2;
BITERATIONS = (500);
MODEL: y1 ON y1&1 y2&1;
y2 ON y2&1 y1&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 for continuous outcomes
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
Y1 Y2
Observed independent variables
Y1&1 Y2&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)
ex6.25.dat
Input data format FREE
SUMMARY OF DATA
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
Number of missing data patterns 2
PROPORTION OF DATA PRESENT
Covariance Coverage
Y1 Y2
________ ________
Y1 1.000
Y2 1.000 1.000
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
Y1 -0.039 -0.213 -2.683 1.00% -1.050 -0.308 0.014
100.000 1.174 -0.511 2.230 1.00% 0.228 1.027
Y2 0.087 0.199 -2.566 1.00% -0.882 -0.103 0.065
100.000 1.083 0.774 3.635 1.00% 0.322 0.996
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 9
Information Criteria
Deviance (DIC) 590.927
Estimated Number of Parameters (pD) 8.792
MODEL RESULTS
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
Y1 ON
Y1&1 0.012 0.110 0.452 -0.200 0.230
Y2&1 -0.237 0.106 0.004 -0.457 -0.037 *
Y2 ON
Y2&1 -0.019 0.098 0.418 -0.216 0.167
Y1&1 0.344 0.096 0.000 0.160 0.548 *
Y2 WITH
Y1 -0.006 0.119 0.482 -0.227 0.232
Intercepts
Y1 -0.019 0.110 0.420 -0.236 0.187
Y2 0.111 0.100 0.130 -0.083 0.297
Residual Variances
Y1 1.201 0.198 0.000 0.902 1.653 *
Y2 1.018 0.158 0.000 0.758 1.373 *
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION
NU
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
0 0 0 0
LAMBDA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0 0 0 0
Y2 0 0 0 0
Y1&1 0 0 0 0
Y2&1 0 0 0 0
THETA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0
Y2 0 0
Y1&1 0 0 0
Y2&1 0 0 0 0
ALPHA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
1 2 0 0
BETA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0 0 3 4
Y2 0 0 5 6
Y1&1 0 0 0 0
Y2&1 0 0 0 0
PSI
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 7
Y2 8 9
Y1&1 0 0 0
Y2&1 0 0 0 0
STARTING VALUES
NU
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
0.000 0.000 0.000 0.000
LAMBDA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 1.000 0.000 0.000 0.000
Y2 0.000 1.000 0.000 0.000
Y1&1 0.000 0.000 1.000 0.000
Y2&1 0.000 0.000 0.000 1.000
THETA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0.000
Y2 0.000 0.000
Y1&1 0.000 0.000 0.000
Y2&1 0.000 0.000 0.000 0.000
ALPHA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
-0.039 0.087 0.000 0.000
BETA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0.000 0.000 0.000 0.000
Y2 0.000 0.000 0.000 0.000
Y1&1 0.000 0.000 0.000 0.000
Y2&1 0.000 0.000 0.000 0.000
PSI
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0.587
Y2 0.000 0.541
Y1&1 0.000 0.000 0.585
Y2&1 0.000 0.000 0.000 0.546
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~IW(0.000,-3) infinity infinity infinity
Parameter 8~IW(0.000,-3) infinity infinity infinity
Parameter 9~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 1.031 2
200 1.001 8
300 1.000 1
400 1.007 2
500 1.001 3
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: 23:12:29
Ending Time: 23:12:29
Elapsed Time: 00:00:00
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