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 first-order autoregressive AR(1)
confirmatory factor analysis (CFA) model
with continuous factor indicators
DATA: FILE = ex6.26.dat;
VARIABLE: NAMES = y1-y4;
ANALYSIS: ESTIMATOR = BAYES;
PROCESSORS = 2;
BITERATIONS = (2000);
MODEL: f BY y1-y4 (&1);
f ON f&1;
OUTPUT: TECH1 TECH8;
PLOT: TYPE = PLOT3;
INPUT READING TERMINATED NORMALLY
this is an example of an N=1 time series analysis
with a first-order autoregressive AR(1)
confirmatory factor analysis (CFA) model
with continuous factor indicators
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 200
Number of dependent variables 4
Number of independent variables 0
Number of continuous latent variables 2
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4
Continuous latent variables
F F&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)
ex6.26.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
Y1 0.096 -0.218 -3.443 0.50% -1.214 -0.218 0.115
200.000 2.000 -0.336 3.146 0.50% 0.456 1.287
Y2 0.179 0.096 -3.403 0.50% -0.965 -0.155 0.091
200.000 2.046 0.068 5.042 0.50% 0.437 1.375
Y3 0.160 -0.095 -4.141 0.50% -1.168 -0.345 0.030
200.000 2.424 -0.447 3.793 0.50% 0.552 1.612
Y4 0.130 -0.186 -4.430 0.50% -1.137 -0.125 0.301
200.000 2.313 0.074 4.586 0.50% 0.657 1.283
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 13
Information Criteria
Deviance (DIC) 2435.851
Estimated Number of Parameters (pD) 167.713
MODEL RESULTS
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
F BY
Y1 1.000 0.000 0.000 1.000 1.000
Y2 1.097 0.125 0.000 0.890 1.391 *
Y3 1.200 0.143 0.000 0.950 1.522 *
Y4 1.118 0.139 0.000 0.882 1.443 *
F ON
F&1 0.310 0.084 0.000 0.152 0.482 *
Intercepts
Y1 0.083 0.118 0.232 -0.164 0.307
Y2 0.166 0.125 0.097 -0.087 0.401
Y3 0.141 0.141 0.154 -0.148 0.411
Y4 0.114 0.133 0.184 -0.162 0.374
Residual Variances
Y1 1.004 0.127 0.000 0.777 1.274 *
Y2 0.890 0.122 0.000 0.669 1.137 *
Y3 1.038 0.149 0.000 0.777 1.355 *
Y4 1.120 0.152 0.000 0.842 1.443 *
F 0.911 0.182 0.000 0.575 1.283 *
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
1 2 3 4
LAMBDA
F F&1
________ ________
Y1 0 0
Y2 5 0
Y3 6 0
Y4 7 0
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 8
Y2 0 9
Y3 0 0 10
Y4 0 0 0 11
ALPHA
F F&1
________ ________
0 0
BETA
F F&1
________ ________
F 0 12
F&1 0 0
PSI
F F&1
________ ________
F 13
F&1 0 0
STARTING VALUES
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
0.096 0.179 0.160 0.130
LAMBDA
F F&1
________ ________
Y1 1.000 0.000
Y2 1.000 0.000
Y3 1.000 0.000
Y4 1.000 0.000
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 1.000
Y2 0.000 1.023
Y3 0.000 0.000 1.212
Y4 0.000 0.000 0.000 1.156
ALPHA
F F&1
________ ________
0.000 0.000
BETA
F F&1
________ ________
F 0.000 0.000
F&1 0.000 0.000
PSI
F F&1
________ ________
F 1.000
F&1 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~IG(-1.000,0.000) infinity infinity infinity
Parameter 9~IG(-1.000,0.000) infinity infinity infinity
Parameter 10~IG(-1.000,0.000) infinity infinity infinity
Parameter 11~IG(-1.000,0.000) infinity infinity infinity
Parameter 12~N(0.000,infinity) 0.0000 infinity infinity
Parameter 13~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 1.279 6
200 1.313 13
300 1.071 13
400 1.014 10
500 1.031 5
600 1.032 5
700 1.052 6
800 1.012 6
900 1.015 4
1000 1.007 4
1100 1.005 4
1200 1.014 7
1300 1.005 2
1400 1.014 7
1500 1.003 12
1600 1.004 8
1700 1.015 7
1800 1.007 7
1900 1.005 8
2000 1.006 5
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
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) 1998-2022 Muthen & Muthen
Back to examples