Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:09 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a random coefficient
regression
DATA: FILE IS ex3.9.dat;
VARIABLE: NAMES ARE y x1 x2;
DEFINE: CENTER x1 x2 (GRANDMEAN);
ANALYSIS: TYPE = RANDOM;
MODEL: s | y ON x1;
s WITH y;
y s ON x2;
INPUT READING TERMINATED NORMALLY
this is an example of a random coefficient
regression
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 1
Number of independent variables 2
Number of continuous latent variables 1
Observed dependent variables
Continuous
Y
Observed independent variables
X1 X2
Continuous latent variables
S
Variables with special functions
Centering (GRANDMEAN)
X1 X2
Estimator MLR
Information matrix OBSERVED
Maximum number of iterations 100
Convergence criterion 0.100D-05
Maximum number of EM iterations 500
Convergence criteria for the EM algorithm
Loglikelihood change 0.100D-02
Relative loglikelihood change 0.100D-05
Derivative 0.100D-03
Minimum variance 0.100D-03
Maximum number of steepest descent iterations 20
Optimization algorithm EMA
Input data file(s)
ex3.9.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
Y 0.582 0.779 -4.162 0.20% -0.935 0.041 0.351
500.000 3.455 1.861 9.755 0.20% 0.749 1.946
X1 0.000 0.029 -2.638 0.20% -0.827 -0.234 0.005
500.000 0.940 -0.246 2.961 0.20% 0.276 0.822
X2 0.000 0.048 -3.020 0.20% -0.896 -0.348 -0.015
500.000 1.117 0.136 3.194 0.20% 0.285 0.850
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 7
Loglikelihood
H0 Value -786.119
H0 Scaling Correction Factor 0.9938
for MLR
Information Criteria
Akaike (AIC) 1586.238
Bayesian (BIC) 1615.740
Sample-Size Adjusted BIC 1593.522
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
S ON
X2 0.251 0.069 3.622 0.000
Y ON
X2 0.594 0.046 12.897 0.000
S WITH
Y 0.650 0.083 7.828 0.000
Intercepts
Y 0.516 0.053 9.741 0.000
S 0.986 0.074 13.334 0.000
Residual Variances
Y 0.847 0.078 10.889 0.000
S 1.160 0.155 7.462 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.391E-01
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:09:25
Ending Time: 23:09:25
Elapsed Time: 00:00:00
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