Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:19 PM
INPUT INSTRUCTIONS
TITLE: growth5.inp normal, covariate, missing
MONTECARLO: NAMES ARE y1-y4 x;
CUTPOINTS = x (0);
NOBSERVATIONS = 1025;
NREPS = 10000;
SEED = 53487;
CLASSES = C(1);
GENCLASSES = C(1);
MISSING = y1-y4;
SAVE = growth5.sav;
ANALYSIS: TYPE = MIXTURE MISSING;
ESTIMATOR = ML;
MODEL MISSING:
%OVERALL%
[y1@-2 y2@-1.5 y3@-1 y4@0];
y2-y4 on x@1;
MODEL MONTECARLO:
%OVERALL%
[x@0]; x@1;
i BY y1-y4@1;
s BY y1@0 y2@1 y3@2 y4@3;
[y1-y4@0];
[i*0 s*.2];
i*.25;
s*.09;
i WITH s*0;
y1-y4*.5;
i ON x*.5;
s ON x*.1;
%C#1%
[i*0 s*.2];
MODEL:
%OVERALL%
i BY y1-y4@1;
s BY y1@0 y2@1 y3@2 y4@3;
[y1-y4@0];
[i*0 s*.2];
i*.25;
s*.09;
i WITH s*0;
y1-y4*.5;
i ON x*.5;
s ON x*.1;
%C#1%
[i*0 s*.2];
OUTPUT: TECH9;
*** WARNING in ANALYSIS command
Starting with Version 5, TYPE=MISSING is the default for all analyses.
To obtain listwise deletion, use LISTWISE=ON in the DATA command.
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
growth5.inp normal, covariate, missing
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1025
Number of replications
Requested 10000
Completed 10000
Value of seed 53487
Number of dependent variables 4
Number of independent variables 1
Number of continuous latent variables 2
Number of categorical latent variables 1
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4
Observed independent variables
X
Continuous latent variables
I S
Categorical latent variables
C
Estimator ML
Information matrix OBSERVED
Optimization Specifications for the Quasi-Newton Algorithm for
Continuous Outcomes
Maximum number of iterations 100
Convergence criterion 0.100D-05
Optimization Specifications for the EM Algorithm
Maximum number of iterations 500
Convergence criteria
Loglikelihood change 0.100D-06
Relative loglikelihood change 0.100D-06
Derivative 0.100D-05
Optimization Specifications for the M step of the EM Algorithm for
Categorical Latent variables
Number of M step iterations 1
M step convergence criterion 0.100D-05
Basis for M step termination ITERATION
Optimization Specifications for the M step of the EM Algorithm for
Censored, Binary or Ordered Categorical (Ordinal), Unordered
Categorical (Nominal) and Count Outcomes
Number of M step iterations 1
M step convergence criterion 0.100D-05
Basis for M step termination ITERATION
Maximum value for logit thresholds 15
Minimum value for logit thresholds -15
Minimum expected cell size for chi-square 0.100D-01
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Optimization algorithm EMA
SUMMARY OF DATA FOR THE FIRST REPLICATION
Number of missing data patterns 15
Number of y missing data patterns 15
Number of u missing data patterns 0
SUMMARY OF MISSING DATA PATTERNS FOR THE FIRST REPLICATION
MISSING DATA PATTERNS FOR Y (x = not missing)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Y1 x x x x x x x x
Y2 x x x x x x x x
Y3 x x x x x x x x
Y4 x x x x x x x x
X x x x x x x x x x x x x x x x
MISSING DATA PATTERN FREQUENCIES FOR Y
Pattern Frequency Pattern Frequency Pattern Frequency
1 168 6 96 11 49
2 29 7 158 12 21
3 96 8 72 13 13
4 30 9 18 14 31
5 235 10 3 15 6
COVARIANCE COVERAGE OF DATA FOR THE FIRST REPLICATION
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT FOR Y
Covariance Coverage
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.881
Y2 0.641 0.739
Y3 0.535 0.443 0.603
Y4 0.334 0.296 0.238 0.380
X 0.881 0.739 0.603 0.380 1.000
SAMPLE STATISTICS FOR THE FIRST REPLICATION
ESTIMATED SAMPLE STATISTICS
Means
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
1 0.246 0.478 0.692 0.896 0.474
Covariances
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.854
Y2 0.375 0.982
Y3 0.369 0.578 1.208
Y4 0.309 0.546 0.924 1.756
X 0.128 0.160 0.180 0.212 0.249
Correlations
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 1.000
Y2 0.409 1.000
Y3 0.363 0.531 1.000
Y4 0.252 0.416 0.635 1.000
X 0.278 0.322 0.328 0.321 1.000
MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -4308.746
TESTS OF MODEL FIT
Number of Free Parameters 11
Loglikelihood
H0 Value
Mean -3564.487
Std Dev 52.355
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 -3686.280 -3688.295
0.980 0.979 -3672.008 -3672.679
0.950 0.949 -3650.605 -3650.994
0.900 0.901 -3631.585 -3631.367
0.800 0.801 -3608.549 -3608.499
0.700 0.700 -3591.942 -3591.987
0.500 0.501 -3564.487 -3564.374
0.300 0.305 -3537.032 -3536.186
0.200 0.203 -3520.425 -3519.848
0.100 0.101 -3497.389 -3497.092
0.050 0.048 -3478.369 -3479.179
0.020 0.019 -3456.966 -3457.934
0.010 0.009 -3442.694 -3447.176
Information Criteria
Akaike (AIC)
Mean 7150.974
Std Dev 104.709
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.991 6907.389 6916.081
0.980 0.981 6935.932 6937.454
0.950 0.952 6978.738 6980.341
0.900 0.899 7016.779 7016.117
0.800 0.797 7062.851 7061.612
0.700 0.695 7096.064 7094.339
0.500 0.499 7150.974 7150.747
0.300 0.300 7205.884 7205.958
0.200 0.199 7239.097 7238.978
0.100 0.099 7285.170 7284.677
0.050 0.051 7323.211 7323.970
0.020 0.021 7366.016 7367.013
0.010 0.011 7394.560 7398.355
Bayesian (BIC)
Mean 7205.231
Std Dev 104.709
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.991 6961.646 6970.338
0.980 0.981 6990.189 6991.711
0.950 0.952 7032.995 7034.598
0.900 0.899 7071.035 7070.374
0.800 0.797 7117.108 7115.869
0.700 0.695 7150.321 7148.596
0.500 0.499 7205.231 7205.004
0.300 0.300 7260.141 7260.214
0.200 0.199 7293.354 7293.235
0.100 0.099 7339.427 7338.934
0.050 0.051 7377.467 7378.227
0.020 0.021 7420.273 7421.270
0.010 0.011 7448.816 7452.612
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 7170.294
Std Dev 104.709
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.991 6926.708 6935.401
0.980 0.981 6955.252 6956.773
0.950 0.952 6998.057 6999.660
0.900 0.899 7036.098 7035.437
0.800 0.797 7082.170 7080.931
0.700 0.695 7115.384 7113.659
0.500 0.499 7170.294 7170.067
0.300 0.300 7225.203 7225.277
0.200 0.199 7258.417 7258.298
0.100 0.099 7304.489 7303.997
0.050 0.051 7342.530 7343.290
0.020 0.021 7385.336 7386.333
0.010 0.011 7413.879 7417.675
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 1025.00000 1.00000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 1025.00000 1.00000
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 1025 1.00000
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1
1 1.000
MODEL RESULTS
ESTIMATES S. E. M. S. E. 95% % Sig
Population Average Std. Dev. Average Cover Coeff
Latent Class 1
I BY
Y1 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
Y2 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
Y3 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
Y4 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
S BY
Y1 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Y2 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
Y3 2.000 2.0000 0.0000 0.0000 0.0000 1.000 0.000
Y4 3.000 3.0000 0.0000 0.0000 0.0000 1.000 0.000
I ON
X 0.500 0.5003 0.0530 0.0527 0.0028 0.949 1.000
S ON
X 0.100 0.0998 0.0357 0.0356 0.0013 0.949 0.801
I WITH
S 0.000 -0.0001 0.0230 0.0228 0.0005 0.949 0.051
Intercepts
Y1 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Y2 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Y3 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Y4 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
I 0.000 -0.0004 0.0366 0.0367 0.0013 0.949 0.051
S 0.200 0.2005 0.0232 0.0231 0.0005 0.946 1.000
Residual Variances
Y1 0.500 0.4992 0.0485 0.0481 0.0024 0.945 1.000
Y2 0.500 0.4998 0.0344 0.0345 0.0012 0.951 1.000
Y3 0.500 0.5000 0.0466 0.0461 0.0022 0.943 1.000
Y4 0.500 0.4993 0.0853 0.0854 0.0073 0.951 1.000
I 0.250 0.2500 0.0449 0.0445 0.0020 0.947 1.000
S 0.090 0.0898 0.0164 0.0165 0.0003 0.950 1.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.358E-02
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
1 0 0 0 0 0
LAMBDA
I S X
________ ________ ________
Y1 0 0 0
Y2 0 0 0
Y3 0 0 0
Y4 0 0 0
X 0 0 0
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 1
Y2 0 2
Y3 0 0 3
Y4 0 0 0 4
X 0 0 0 0 0
ALPHA
I S X
________ ________ ________
1 5 6 0
BETA
I S X
________ ________ ________
I 0 0 7
S 0 0 8
X 0 0 0
PSI
I S X
________ ________ ________
I 9
S 10 11
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 0
GAMMA(C)
X
________
C#1 0
STARTING VALUES FOR LATENT CLASS 1
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
1 0.000 0.200 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.100
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 0.250
S 0.000 0.090
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 0.000
GAMMA(C)
X
________
C#1 0.000
POPULATION VALUES FOR LATENT CLASS 1
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
1 0.000 0.200 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.100
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 0.250
S 0.000 0.090
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 0.000
GAMMA(C)
X
________
C#1 0.000
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
REPLICATION 5427:
WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IN CLASS 1
IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/
RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL
TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE
THAN TWO LATENT VARIABLES. CHECK THE TECH4 OUTPUT FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE S.
SAVEDATA INFORMATION
Order of variables
Y1
Y2
Y3
Y4
X
C
Save file
growth5.sav
Save file format Free
Save file record length 5000
Missing designated by 999
Beginning Time: 23:19:23
Ending Time: 23:21:18
Elapsed Time: 00:01:55
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