Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:12 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a growth model with
individually-varying times of observation
and a random slope for time-varying
covariates for a continuous outcome
DATA: FILE IS ex6.12.dat;
VARIABLE: NAMES ARE y1-y4 x a21-a24 a11-a14;
TSCORES = a11-a14;
ANALYSIS: TYPE = RANDOM;
MODEL: i s | y1-y4 AT a11-a14;
st | y1 ON a21;
st | y2 ON a22;
st | y3 ON a23;
st | y4 ON a24;
i s st ON x;
INPUT READING TERMINATED NORMALLY
this is an example of a growth model with
individually-varying times of observation
and a random slope for time-varying
covariates for a continuous outcome
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 4
Number of independent variables 5
Number of continuous latent variables 3
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4
Observed independent variables
X A21 A22 A23 A24
Continuous latent variables
ST I S
Variables with special functions
Time scores
A11 A12 A13 A14
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)
ex6.12.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 0.426 0.231 -3.440 0.20% -0.915 0.016 0.405
500.000 2.391 -0.094 5.031 0.20% 0.805 1.630
Y2 1.459 0.298 -4.294 0.20% -0.135 0.961 1.420
500.000 3.515 0.448 8.201 0.20% 1.846 3.023
Y3 2.485 0.106 -3.981 0.20% 0.757 1.969 2.456
500.000 4.739 0.385 9.931 0.20% 3.077 4.155
Y4 3.413 0.149 -3.976 0.20% 1.234 2.747 3.313
500.000 6.973 0.277 12.565 0.20% 3.990 5.650
X -0.029 -0.090 -3.542 0.20% -0.850 -0.252 -0.015
500.000 1.063 0.122 3.167 0.20% 0.259 0.795
A21 -0.038 -0.157 -3.990 0.20% -0.935 -0.301 -0.018
500.000 1.079 0.258 2.840 0.20% 0.205 0.865
A22 0.011 0.086 -2.628 0.20% -0.867 -0.233 0.011
500.000 1.052 0.137 3.610 0.20% 0.237 0.826
A23 0.007 -0.008 -2.739 0.20% -0.753 -0.232 -0.011
500.000 0.943 0.262 3.770 0.20% 0.217 0.820
A24 -0.059 -0.043 -2.925 0.20% -0.948 -0.313 -0.058
500.000 1.033 -0.444 2.681 0.20% 0.185 0.866
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 14
Loglikelihood
H0 Value -3166.918
H0 Scaling Correction Factor 1.0203
for MLR
Information Criteria
Akaike (AIC) 6361.835
Bayesian (BIC) 6420.840
Sample-Size Adjusted BIC 6376.403
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
I ON
X 0.697 0.051 13.567 0.000
S ON
X 0.333 0.025 13.389 0.000
ST ON
X 0.156 0.035 4.411 0.000
S WITH
I 0.025 0.032 0.802 0.423
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
ST 0.453 0.036 12.706 0.000
I 0.463 0.051 9.043 0.000
S 1.007 0.025 40.749 0.000
Residual Variances
Y1 0.468 0.064 7.347 0.000
Y2 0.501 0.058 8.674 0.000
Y3 0.405 0.058 7.035 0.000
Y4 0.556 0.092 6.044 0.000
ST 0.366 0.041 8.997 0.000
I 0.870 0.082 10.645 0.000
S 0.175 0.020 8.785 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.176E-01
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:12:16
Ending Time: 23:12:16
Elapsed Time: 00:00:00
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