Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:18 PM
INPUT INSTRUCTIONS
TITLE: growth4.inp normal, covariate, no missing
MONTECARLO: NAMES ARE y1-y4 x;
CUTPOINTS = x (0);
NOBSERVATIONS = 600;
NREPS = 10000;
SEED = 53487;
CLASSES = C(1);
GENCLASSES = C(1);
SAVE = growth4.sav;
ANALYSIS: TYPE = MIXTURE;
ESTIMATOR = ML;
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;
INPUT READING TERMINATED NORMALLY
growth4.inp normal, covariate, no missing
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 600
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
Optimization algorithm EMA
SAMPLE STATISTICS FOR THE FIRST REPLICATION
SAMPLE STATISTICS
Means
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
1 0.252 0.500 0.719 0.946 0.470
Covariances
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.843
Y2 0.345 1.000
Y3 0.371 0.633 1.301
Y4 0.399 0.737 1.077 1.923
X 0.138 0.154 0.183 0.205 0.250
Correlations
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 1.000
Y2 0.376 1.000
Y3 0.354 0.555 1.000
Y4 0.313 0.531 0.681 1.000
X 0.300 0.309 0.322 0.296 1.000
TESTS OF MODEL FIT
Number of Free Parameters 11
Loglikelihood
H0 Value
Mean -3165.742
Std Dev 34.668
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 -3246.392 -3246.836
0.980 0.980 -3236.941 -3237.055
0.950 0.951 -3222.768 -3222.390
0.900 0.901 -3210.173 -3209.952
0.800 0.798 -3194.919 -3195.197
0.700 0.701 -3183.923 -3183.712
0.500 0.501 -3165.742 -3165.682
0.300 0.301 -3147.562 -3147.508
0.200 0.202 -3136.566 -3136.338
0.100 0.101 -3121.311 -3121.142
0.050 0.049 -3108.716 -3109.269
0.020 0.021 -3094.544 -3094.285
0.010 0.011 -3085.093 -3084.392
Information Criteria
Akaike (AIC)
Mean 6353.485
Std Dev 69.337
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 6192.187 6190.420
0.980 0.979 6211.088 6210.539
0.950 0.951 6239.433 6240.465
0.900 0.899 6264.623 6264.212
0.800 0.798 6295.131 6294.617
0.700 0.699 6317.125 6316.978
0.500 0.499 6353.485 6353.361
0.300 0.299 6389.845 6389.400
0.200 0.202 6411.839 6412.339
0.100 0.099 6442.347 6441.859
0.050 0.049 6467.537 6466.603
0.020 0.020 6495.882 6496.052
0.010 0.010 6514.783 6515.635
Bayesian (BIC)
Mean 6401.851
Std Dev 69.337
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 6240.553 6238.786
0.980 0.979 6259.454 6258.905
0.950 0.951 6287.799 6288.831
0.900 0.899 6312.989 6312.578
0.800 0.798 6343.497 6342.983
0.700 0.699 6365.491 6365.345
0.500 0.499 6401.851 6401.727
0.300 0.299 6438.211 6437.766
0.200 0.202 6460.205 6460.706
0.100 0.099 6490.713 6490.225
0.050 0.049 6515.903 6514.970
0.020 0.020 6544.248 6544.418
0.010 0.010 6563.149 6564.001
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 6366.929
Std Dev 69.337
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 6205.631 6203.864
0.980 0.979 6224.532 6223.983
0.950 0.951 6252.877 6253.909
0.900 0.899 6278.067 6277.656
0.800 0.798 6308.575 6308.061
0.700 0.699 6330.569 6330.423
0.500 0.499 6366.929 6366.805
0.300 0.299 6403.289 6402.844
0.200 0.202 6425.283 6425.784
0.100 0.099 6455.791 6455.303
0.050 0.049 6480.981 6480.048
0.020 0.020 6509.326 6509.496
0.010 0.010 6528.227 6529.079
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 600.00000 1.00000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 600.00000 1.00000
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 600 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 0.5004 0.0638 0.0632 0.0041 0.943 1.000
S ON
X 0.100 0.1002 0.0357 0.0356 0.0013 0.947 0.808
I WITH
S 0.000 0.0002 0.0192 0.0190 0.0004 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 -0.0002 0.0451 0.0447 0.0020 0.947 0.053
S 0.200 0.2001 0.0254 0.0251 0.0006 0.946 1.000
Residual Variances
Y1 0.500 0.4999 0.0488 0.0485 0.0024 0.950 1.000
Y2 0.500 0.4999 0.0348 0.0352 0.0012 0.949 1.000
Y3 0.500 0.4996 0.0400 0.0394 0.0016 0.945 1.000
Y4 0.500 0.5002 0.0630 0.0628 0.0040 0.948 1.000
I 0.250 0.2487 0.0432 0.0428 0.0019 0.948 1.000
S 0.090 0.0896 0.0134 0.0133 0.0002 0.945 1.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.459E-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)
SAVEDATA INFORMATION
Order of variables
Y1
Y2
Y3
Y4
X
C
Save file
growth4.sav
Save file format Free
Save file record length 5000
Beginning Time: 23:18:17
Ending Time: 23:19:23
Elapsed Time: 00:01:06
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