Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:11 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a Bayesian MIMIC model with cross-loadings and direct effects
DATA: FILE = ex5.32.dat;
VARIABLE: NAMES = y1-y6 x1-x3;
ANALYSIS: ESTIMATOR = BAYES;
PROCESSORS = 2;
MODEL: f1 BY y1-y3
y4-y6 (xload4-xload6);
f2 BY y4-y6
y1-y3 (xload1-xload3);
f1-f2 ON x1-x3;
y1-y6 ON x1-x3 (dir1-dir18);
MODEL PRIORS:
xload1-xload6~N(0,0.01);
dir1-dir18~N(0,0.01);
PLOT: TYPE = PLOT2;
INPUT READING TERMINATED NORMALLY
this is an example of a Bayesian MIMIC model with cross-loadings and direct effects
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 6
Number of independent variables 3
Number of continuous latent variables 2
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4 Y5 Y6
Observed independent variables
X1 X2 X3
Continuous latent variables
F1 F2
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)
ex5.32.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 2.066 -0.174 -2.827 0.20% 0.516 1.668 2.111
500.000 3.227 -0.300 6.876 0.20% 2.616 3.685
Y2 2.088 0.021 -2.785 0.20% 0.544 1.611 2.178
500.000 3.388 -0.114 7.456 0.20% 2.561 3.658
Y3 2.088 0.036 -2.223 0.20% 0.534 1.685 2.046
500.000 3.295 -0.323 6.987 0.20% 2.510 3.553
Y4 1.663 -0.017 -3.780 0.20% 0.067 1.162 1.694
500.000 3.595 -0.233 7.734 0.20% 2.122 3.307
Y5 1.623 -0.138 -3.931 0.20% 0.039 1.236 1.649
500.000 3.290 -0.253 5.979 0.20% 2.060 3.303
Y6 1.596 -0.104 -3.491 0.20% 0.040 1.122 1.588
500.000 3.434 -0.338 6.396 0.20% 2.087 3.243
X1 -0.061 -0.070 -5.568 0.20% -1.405 -0.513 -0.093
500.000 2.921 0.161 4.844 0.20% 0.268 1.410
X2 1.033 -0.175 -4.642 0.20% -0.089 0.776 1.082
500.000 2.082 0.136 4.880 0.20% 1.424 2.167
X3 2.090 0.036 -0.597 0.20% 1.176 1.777 2.115
500.000 1.042 -0.377 4.840 0.20% 2.374 3.002
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 49
Bayesian Posterior Predictive Checking using Chi-Square
95% Confidence Interval for the Difference Between
the Observed and the Replicated Chi-Square Values
-26.748 30.445
Posterior Predictive P-Value 0.572
Prior Posterior Predictive P-Value 0.927
Information Criteria
Deviance (DIC) 8064.188
Estimated Number of Parameters (pD) 38.654
Bayesian (BIC) 8291.205
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
90 Percent C.I. 0.000 0.068
Probability RMSEA <= .05 0.842
CFI/TLI
CFI 1.000
90 Percent C.I. 0.996 1.000
TLI 1.000
90 Percent C.I. 0.980 1.000
MODEL RESULTS
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
F1 BY
Y1 1.000 0.000 0.000 1.000 1.000
Y2 1.040 0.072 0.000 0.912 1.188 *
Y3 1.001 0.069 0.000 0.877 1.143 *
Y4 0.065 0.065 0.154 -0.055 0.193
Y5 -0.049 0.072 0.269 -0.181 0.100
Y6 -0.008 0.063 0.444 -0.126 0.121
F2 BY
Y4 1.000 0.000 0.000 1.000 1.000
Y5 1.077 0.078 0.000 0.938 1.227 *
Y6 0.945 0.071 0.000 0.811 1.085 *
Y1 -0.009 0.072 0.439 -0.158 0.122
Y2 -0.002 0.074 0.487 -0.147 0.134
Y3 0.037 0.072 0.298 -0.111 0.176
F1 ON
X1 0.483 0.079 0.000 0.327 0.635 *
X2 0.557 0.069 0.000 0.438 0.699 *
X3 0.706 0.072 0.000 0.583 0.903 *
F2 ON
X1 0.646 0.072 0.000 0.480 0.773 *
X2 0.573 0.063 0.000 0.445 0.691 *
X3 0.433 0.062 0.000 0.308 0.552 *
Y1 ON
X1 0.012 0.064 0.429 -0.105 0.141
X2 0.019 0.057 0.363 -0.088 0.130
X3 0.000 0.062 0.497 -0.128 0.118
Y2 ON
X1 0.005 0.065 0.474 -0.121 0.132
X2 0.013 0.060 0.415 -0.106 0.124
X3 -0.033 0.063 0.297 -0.164 0.086
Y3 ON
X1 0.006 0.062 0.460 -0.113 0.127
X2 -0.016 0.060 0.409 -0.134 0.097
X3 -0.005 0.062 0.471 -0.129 0.111
Y4 ON
X1 0.017 0.064 0.403 -0.101 0.156
X2 -0.030 0.059 0.301 -0.142 0.091
X3 -0.029 0.057 0.313 -0.146 0.072
Y5 ON
X1 -0.009 0.065 0.451 -0.123 0.127
X2 -0.014 0.065 0.418 -0.136 0.111
X3 -0.016 0.060 0.397 -0.131 0.101
Y6 ON
X1 0.053 0.062 0.193 -0.065 0.176
X2 0.085 0.061 0.085 -0.032 0.200
X3 0.018 0.059 0.375 -0.097 0.136
F2 WITH
F1 0.224 0.080 0.005 0.068 0.379 *
Intercepts
Y1 0.037 0.110 0.383 -0.173 0.257
Y2 0.035 0.112 0.369 -0.184 0.258
Y3 0.031 0.111 0.403 -0.173 0.243
Y4 0.166 0.113 0.067 -0.046 0.397
Y5 0.194 0.102 0.024 0.001 0.396 *
Y6 0.117 0.107 0.147 -0.087 0.324
Residual Variances
Y1 0.536 0.051 0.000 0.436 0.634 *
Y2 0.516 0.053 0.000 0.419 0.629 *
Y3 0.496 0.048 0.000 0.407 0.594 *
Y4 0.560 0.050 0.000 0.466 0.661 *
Y5 0.360 0.047 0.000 0.275 0.461 *
Y6 0.551 0.048 0.000 0.463 0.653 *
F1 0.735 0.091 0.000 0.583 0.935 *
F2 0.624 0.079 0.000 0.484 0.794 *
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
Beginning Time: 23:11:25
Ending Time: 23:11:26
Elapsed Time: 00:00:01
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