Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:12 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a linear growth model
for a continuous outcome with first-order auto correlated
residuals using non-linear constraints
DATA: FILE = ex6.17.dat;
VARIABLE: NAMES = y1-y4;
MODEL: i s | y1@0 y2@1 y3@2 y4@3;
y1-y4 (resvar);
y1-y3 PWITH y2-y4 (p1);
y1-y2 PWITH y3-y4 (p2);
y1 WITH y4 (p3);
MODEL CONSTRAINT:
NEW (corr);
p1 = resvar*corr;
p2 = resvar*corr**2;
p3 = resvar*corr**3;
INPUT READING TERMINATED NORMALLY
this is an example of a linear growth model
for a continuous outcome with first-order auto correlated
residuals using non-linear constraints
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1000
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
I S
Estimator ML
Information matrix OBSERVED
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Maximum number of steepest descent iterations 20
Input data file(s)
ex6.17.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 0.016 -0.084 -3.687 0.10% -1.132 -0.303 0.040
1000.000 1.897 -0.258 3.792 0.10% 0.356 1.232
Y2 1.027 0.027 -4.043 0.10% -0.230 0.641 1.018
1000.000 2.295 -0.038 5.854 0.10% 1.391 2.277
Y3 2.001 0.047 -3.213 0.10% 0.509 1.454 1.958
1000.000 2.982 -0.329 8.140 0.10% 2.467 3.566
Y4 3.053 0.044 -3.040 0.10% 1.292 2.538 3.081
1000.000 4.006 -0.019 9.244 0.10% 3.546 4.687
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 7
Loglikelihood
H0 Value -6593.460
H1 Value -6592.226
Information Criteria
Akaike (AIC) 13200.921
Bayesian (BIC) 13235.275
Sample-Size Adjusted BIC 13213.043
(n* = (n + 2) / 24)
Chi-Square Test of Model Fit
Value 2.469
Degrees of Freedom 7
P-Value 0.9294
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
90 Percent C.I. 0.000 0.012
Probability RMSEA <= .05 1.000
CFI/TLI
CFI 1.000
TLI 1.000
Chi-Square Test of Model Fit for the Baseline Model
Value 2118.221
Degrees of Freedom 6
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value 0.006
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
I |
Y1 1.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 1.000 0.000 999.000 999.000
Y4 1.000 0.000 999.000 999.000
S |
Y1 0.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 2.000 0.000 999.000 999.000
Y4 3.000 0.000 999.000 999.000
S WITH
I 0.139 0.052 2.680 0.007
Y1 WITH
Y2 0.734 0.209 3.507 0.000
Y3 0.368 0.154 2.397 0.017
Y4 0.185 0.102 1.820 0.069
Y2 WITH
Y3 0.734 0.209 3.507 0.000
Y4 0.368 0.154 2.397 0.017
Y3 WITH
Y4 0.734 0.209 3.507 0.000
Means
I 0.011 0.042 0.266 0.790
S 1.011 0.021 49.078 0.000
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
Variances
I 0.424 0.253 1.676 0.094
S 0.142 0.034 4.135 0.000
Residual Variances
Y1 1.462 0.225 6.490 0.000
Y2 1.462 0.225 6.490 0.000
Y3 1.462 0.225 6.490 0.000
Y4 1.462 0.225 6.490 0.000
New/Additional Parameters
CORR 0.502 0.067 7.543 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.159E-03
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:12:19
Ending Time: 23:12:19
Elapsed Time: 00:00:00
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